import pandas as pd
import numpy  as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings

# Get the names of the sheets in the Excel file
# url = 'https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1166194/2005-21-uk-local-authority-ghg-emissions.xlsx'
# data_1_1 = pd.read_excel(url, sheet_name='1_1')

sheet_names = pd.ExcelFile("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx").sheet_names
sheet_names

['Cover',
 'Contents',
 '1_1',
 '1_2',
 '1_3',
 '1_4',
 '2_1',
 '3_1',
 '3_2',
 '4_1',
 '4_1_Notes',
 '4_2',
 '4_3',
 '4_4',
 '4_5',
 '5_1']

# Load the first few rows from the '1_1' sheet to explore its contents

#url = 'https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1166194/2005-21-uk-local-authority-ghg-emissions.xlsx'
#data_1_1 = pd.read_excel(url, sheet_name='1_1')

data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')
data_1_1.head()

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

data_1_1_actual

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)  
data_1_1_actual

data_1_1_actual

# Display the shape of the data
data_shape = data_1_1_actual.shape

data_shape

(7106, 50)

# Get a summary of the data
data_summary = data_1_1_actual.describe()

data_summary

# Get a view of the datatypes

data_types = data_1_1_actual.dtypes
data_types

3
Region/Country                            object
Second Tier Authority                     object
Local Authority                           object
Local Authority Code                      object
Calendar Year                             object
Industry Electricity                      object
Industry Gas                              object
Large Industrial Installations            object
Industry 'Other'                          object
Industry Total                            object
Commercial Electricity                    object
Commercial Gas                            object
Commercial 'Other'                        object
Commercial Total                          object
Public Sector Electricity                 object
Public Sector Gas                         object
Public Sector 'Other'                     object
Public Sector Total                       object
Domestic Electricity                      object
Domestic Gas                              object
Domestic 'Other'                          object
Domestic Total                            object
Road Transport (A roads)                  object
Road Transport (Motorways)                object
Road Transport (Minor roads)              object
Diesel Railways                           object
Transport 'Other'                         object
Transport Total                           object
Net Emissions: Forest land                object
Net Emissions: Cropland                   object
Net Emissions: Grassland                  object
Net Emissions: Wetlands                   object
Net Emissions: Settlements                object
Net Emissions: Harvested Wood Products    object
Net Emissions: Indirect N2O               object
LULUCF Net Emissions                      object
Agriculture Electricity                   object
Agriculture Gas                           object
Agriculture 'Other'                       object
Agriculture Livestock                     object
Agriculture Soils                         object
Agriculture Total                         object
Landfill                                  object
Waste Management 'Other'                  object
Waste Management Total                    object
Grand Total                               object
Population ('000s, mid-year estimate)     object
Per Capita Emissions (tCO2e)              object
Area (km2)                                object
Emissions per km2 (kt CO2e)               object
dtype: object

# Check for missing values
missing_values = data_1_1_actual.isnull().sum()

missing_values

3
Region/Country                              0
Second Tier Authority                       0
Local Authority                             0
Local Authority Code                      748
Calendar Year                               0
Industry Electricity                        0
Industry Gas                                0
Large Industrial Installations              0
Industry 'Other'                            0
Industry Total                              0
Commercial Electricity                      0
Commercial Gas                              0
Commercial 'Other'                          0
Commercial Total                            0
Public Sector Electricity                   0
Public Sector Gas                           0
Public Sector 'Other'                       0
Public Sector Total                         0
Domestic Electricity                        0
Domestic Gas                                0
Domestic 'Other'                            0
Domestic Total                              0
Road Transport (A roads)                    0
Road Transport (Motorways)                  0
Road Transport (Minor roads)                0
Diesel Railways                             0
Transport 'Other'                           0
Transport Total                             0
Net Emissions: Forest land                  0
Net Emissions: Cropland                     0
Net Emissions: Grassland                    0
Net Emissions: Wetlands                     0
Net Emissions: Settlements                  0
Net Emissions: Harvested Wood Products      0
Net Emissions: Indirect N2O                 0
LULUCF Net Emissions                        0
Agriculture Electricity                     0
Agriculture Gas                             0
Agriculture 'Other'                         0
Agriculture Livestock                       0
Agriculture Soils                           0
Agriculture Total                           0
Landfill                                    0
Waste Management 'Other'                    0
Waste Management Total                      0
Grand Total                                 0
Population ('000s, mid-year estimate)       0
Per Capita Emissions (tCO2e)                0
Area (km2)                                  0
Emissions per km2 (kt CO2e)                 0
dtype: int64

import matplotlib.pyplot as plt

# Group by 'Calendar Year' and sum the 'Grand Total' column to get total emissions for each year
yearly_emissions = data_1_1_actual.groupby('Calendar Year')['Grand Total'].sum()

# Plotting the trend over time
plt.figure(figsize=(12, 6))
yearly_emissions.plot(marker='o', linestyle='-', color='black')
plt.title('Total National Emissions Trend Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.grid(True)
plt.show()

# Convert the 'Grand Total' column to a numeric type
#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_numeric.html
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Group by 'Local Authority Region/Country' and sum the 'Grand Total' column to get total emissions for each region again
#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html
regional_emissions = data_1_1_actual.groupby('Region/Country')['Grand Total'].sum()

# Sort the regional emissions in descending order and get the name of the first region
#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html
highest_emission_region = regional_emissions.sort_values(ascending=False).index[0]
highest_emission_value = regional_emissions.sort_values(ascending=False).iloc[0]

# Sort the regional emissions in ascending order and get the name of the first region
lowest_emission_region = regional_emissions.sort_values(ascending=True).index[0]
lowest_emission_value = regional_emissions.sort_values(ascending=True).iloc[0]

# Results
highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value

('National Total', 8763483.862937795, 'Unallocated', 70997.7272303627)

regional_emissions

Region/Country
East Midlands                     1.246579e+06
East Midlands Total               6.759135e+05
East of England                   1.518514e+06
East of England Total             7.986982e+05
England Total                     6.830838e+06
London                            7.105470e+05
London Total                      7.105470e+05
National Total                    8.763484e+06
North East                        4.513099e+05
North East Total                  4.513099e+05
North West                        1.419484e+06
North West Total                  9.620017e+05
Northern Ireland                  3.910081e+05
Northern Ireland Total            3.910081e+05
Scotland                          8.602071e+05
Scotland Total                    8.602071e+05
South East                        1.713013e+06
South East Total                  1.001996e+06
South West                        1.073928e+06
South West Total                  6.987242e+05
Unallocated                       7.099773e+04
Wales                             6.104329e+05
Wales Total                       6.104329e+05
West Midlands                     1.030287e+06
West Midlands Total               7.107770e+05
Yorkshire and the Humber          9.443414e+05
Yorkshire and the Humber Total    8.208705e+05
Name: Grand Total, dtype: float64

import matplotlib.pyplot as plt 

plt.figure(figsize=(10, 6))
regional_emissions.plot(kind='bar', color='blue')
plt.title('Total Emissions by Region')
plt.xlabel('Region/Country')
plt.ylabel('Total Emissions')
plt.xticks(rotation=90)
plt.grid(axis='y')
plt.show()

# Drop 'National Total','England Total' and 'Unallocated' from the Series
regional_emissions = regional_emissions.drop(labels=['National Total','England Total', 'Unallocated'])

plt.figure(figsize=(10, 6))
regional_emissions.plot(kind='bar', color='blue')
plt.title('Total Emissions by Region')
plt.xlabel('Region/Country')
plt.ylabel('Total Emissions')
plt.xticks(rotation=90)
plt.grid(axis='y')
plt.show()

# Drop 'National Total' and 'Unallocated' from the Series
#regional_emissions = regional_emissions.drop(labels=['National Total','England Total', 'Unallocated'])
#drop labels that end with total

# Convert 'Grand Total' to numeric, coercing errors to NaN
#data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Exclude rows where 'Region/Country' ends with 'total' 
#data_1_1_filtered = data_1_1_actual[
#    ~data_1_1_actual['Region/Country'].str.lower().str.endswith('total')
#]

# Group by 'Region/Country' and sum 'Grand Total' for remaining regions
#regional_emissions = data_1_1_filtered.groupby('Region/Country')['Grand Total'].sum()

# Drop 'Unallocated' from the Series
#regional_emissions = regional_emissions.drop(labels=['Unallocated'])

# Identify the highest and lowest emission regions
#highest_emission_region = regional_emissions.idxmax()
#lowest_emission_region = regional_emissions.idxmin()

# Get the highest and lowest emission values
#highest_emission_value = regional_emissions[highest_emission_region]
#lowest_emission_value = regional_emissions[lowest_emission_region]

# Results
#highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Filter out rows where 'Region/Country' ends with 'Total' or is 'Unallocated'
data_1_1_filtered = data_1_1_actual[
    ~data_1_1_actual['Region/Country'].str.endswith('Total') &
    ~data_1_1_actual['Region/Country'].str.contains('Unallocated', case=False)
]

# Group by 'Region/Country' and sum the 'Grand Total' column
regional_emissions = data_1_1_filtered.groupby('Region/Country')['Grand Total'].sum()

# Identify the regions with the highest and lowest cumulative emissions
highest_emission_region = regional_emissions.idxmax()
lowest_emission_region = regional_emissions.idxmin()

# Get the corresponding values for the highest and lowest emissions
highest_emission_value = regional_emissions[highest_emission_region]
lowest_emission_value = regional_emissions[lowest_emission_region]

# Output the results
(highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value)

('South East', 1713012.733123874, 'Northern Ireland', 391008.14327239833)

import matplotlib.pyplot as plt

# regional_emissions
# regional_emissions = data_1_1_filtered.groupby('Region/Country')['Grand Total'].sum()

plt.figure(figsize=(10, 6))
regional_emissions.plot(kind='bar', color='blue')
plt.title('Total Emissions by Region')
plt.xlabel('Region/Country')
plt.ylabel('Total Emissions')
plt.xticks(rotation=90)
plt.grid(axis='y')
plt.show()

regional_emissions

Region/Country
East Midlands               1.246579e+06
East of England             1.518514e+06
London                      7.105470e+05
North East                  4.513099e+05
North West                  1.419484e+06
Northern Ireland            3.910081e+05
Scotland                    8.602071e+05
South East                  1.713013e+06
South West                  1.073928e+06
Wales                       6.104329e+05
West Midlands               1.030287e+06
Yorkshire and the Humber    9.443414e+05
Name: Grand Total, dtype: float64

#import matplotlib.pyplot as plt
#import pandas as pd

#regions = ['East Midlands', 'East of England', 'London', 'North East', 'North West', 
#           'Northern Ireland', 'Scotland', 'South East', 'South West', 'Wales', 
#           'West Midlands', 'Yorkshire and the Humber']
#emissions = [1.246579e+06, 1.518514e+06, 7.105470e+05, 4.513099e+05, 1.419484e+06, 
#             3.910081e+05, 8.602071e+05, 1.713013e+06, 1.073928e+06, 6.104329e+05, 
#             1.030287e+06, 9.443414e+05]

#emissions_series = pd.Series(emissions, index=regions)

# Define colors, with red for 'West Midlands' and blue for others
#colors = ['red' if region == 'West Midlands' else 'blue' for region in regions]

# Create the bar plot
#plt.figure(figsize=(10, 6))
#emissions_series.plot(kind='bar', color=colors)
#plt.title('Total Emissions by Region')
#plt.xlabel('Region/Country')
#plt.ylabel('Grand Total Emissions')
#plt.xticks(rotation=90)
#plt.tight_layout()  
#plt.grid(axis='y')
# plt.savefig('emissions_by_region.png')

# Display the plot
#plt.show()

import matplotlib.pyplot as plt
import pandas as pd

# Sort the series in ascending order for the plot
sorted_emissions = regional_emissions.sort_values()

# Define the colors for the bars, 'red' for 'West Midlands Total', 'skyblue' for the rest
colors = ['red' if region == 'West Midlands' else 'lightblue' for region in sorted_emissions.index]

# Create the bar plot
plt.figure(figsize=(10, 6))
sorted_emissions.plot(kind='bar', color=colors)

# Add titles and labels
plt.title('Total Emissions by Region (Sorted Ascending)', fontsize=14)
plt.xlabel('Region/Country', fontsize=12)
plt.ylabel('Total Emissions', fontsize=12)
plt.xticks(rotation=90)  
plt.grid(axis='y')

# Show the plot
plt.tight_layout()  # Adjust layout to fit labels
plt.show()

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Group by 'Region/Country' and sum the 'Grand Total' column
regional_emissions_total = data_1_1_actual.groupby('Region/Country')['Grand Total'].sum()

# Drop 'National Total' and 'Unallocated' from the Series
regional_emissions_total = regional_emissions_total.drop(labels=['National Total', 'Unallocated'])

# Identify the regions with the highest and lowest cumulative emissions
highest_emission_region = regional_emissions_total.idxmax()
lowest_emission_region = regional_emissions_total.idxmin()

# Get the corresponding values for the highest and lowest emissions
highest_emission_value = regional_emissions_total[highest_emission_region]
lowest_emission_value = regional_emissions_total[lowest_emission_region]

# Output the results
(highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value)

('England Total', 6830837.969058414, 'Northern Ireland', 391008.14327239833)

# Keep only rows where 'Region/Country' ends with 'total' (case insensitive)
#data_1_1_filtered = data_1_1_actual[
#    data_1_1_actual['Region/Country'].str.lower().str.endswith('total')
#]

# Convert 'Grand Total' to numeric, coercing errors to NaN
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Keep only rows where 'Region/Country' ends with 'total' 
data_1_1_filtered = data_1_1_actual[
    data_1_1_actual['Region/Country'].str.lower().str.endswith('total')
]


# Group by 'Region/Country' and sum 'Grand Total' for remaining regions
regional_emissions_total = data_1_1_filtered.groupby('Region/Country')['Grand Total'].sum()

# Drop 'Unallocated' from the Series
#regional_emissions_total = regional_emissions_total.drop(labels=['Unallocated'])

# Drop 'National Total' from the Series
regional_emissions_total = regional_emissions_total.drop(labels=['National Total'])

# Drop 'England Total' from the Series
regional_emissions_total = regional_emissions_total.drop(labels=['England Total'])

# Identify the highest and lowest emission regions
highest_emission_region = regional_emissions_total.idxmax()
lowest_emission_region = regional_emissions_total.idxmin()

# Get the highest and lowest emission values
highest_emission_value = regional_emissions_total[highest_emission_region]
lowest_emission_value = regional_emissions_total[lowest_emission_region]

# Results
highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value

('South East Total',
 1001996.0574329,
 'Northern Ireland Total',
 391008.14327239833)

# Sort the regional emissions in descending order and get the name of the first region
highest_emission_region = regional_emissions.sort_values(ascending=False).index[0]
highest_emission_value = regional_emissions.sort_values(ascending=False).iloc[0]

# Sort the regional emissions in ascending order and get the name of the first region
lowest_emission_region = regional_emissions.sort_values(ascending=True).index[0]
lowest_emission_value = regional_emissions.sort_values(ascending=True).iloc[0]

# Results
highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value

('South East', 1713012.733123874, 'Northern Ireland', 391008.14327239833)

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
regional_emissions_total.plot(kind='bar', color='blue')
plt.title('Total Emissions by Region Total')
plt.xlabel('Region/Country')
plt.ylabel('Total Emissions')
plt.xticks(rotation=90)
plt.grid(axis='y')
plt.show()

regional_emissions_total

Region/Country
East Midlands Total               6.759135e+05
East of England Total             7.986982e+05
London Total                      7.105470e+05
North East Total                  4.513099e+05
North West Total                  9.620017e+05
Northern Ireland Total            3.910081e+05
Scotland Total                    8.602071e+05
South East Total                  1.001996e+06
South West Total                  6.987242e+05
Wales Total                       6.104329e+05
West Midlands Total               7.107770e+05
Yorkshire and the Humber Total    8.208705e+05
Name: Grand Total, dtype: float64

# Convert the dictionary to a pandas Series
emissions_series = pd.Series(regional_emissions_total)

# Define the colors for the bars, 'red' for 'West Midlands Total', 'skyblue' for the rest
colors = ['red' if region == 'West Midlands Total' else 'blue' for region in emissions_series.index]

# Create the bar plot
plt.figure(figsize=(10, 6))
emissions_series.plot(kind='bar', color=colors)

# Add titles and labels
plt.title('Total Emissions by Region Total', fontsize=14)
plt.xlabel('Region/Country', fontsize=12)
plt.ylabel('Total Emissions', fontsize=12)
plt.xticks(rotation=90)  
plt.grid(axis='y')
# plt.savefig('emissions_by_region_total.png')

# Show the plot
plt.tight_layout()  # Adjust layout to fit labels
plt.show()

import matplotlib.pyplot as plt
import pandas as pd

# Sort the series in ascending order for the plot
sorted_emissions = regional_emissions_total.sort_values()

# Define the colors for the bars, 'red' for 'West Midlands Total', 'skyblue' for the rest
colors = ['red' if region == 'West Midlands Total' else 'lightblue' for region in sorted_emissions.index]

# Create the bar plot
plt.figure(figsize=(10, 6))
sorted_emissions.plot(kind='bar', color=colors)

# Add titles and labels
plt.title('Total Emissions by Region Total (Sorted Ascending)', fontsize=14)
plt.xlabel('Region/Country', fontsize=12)
plt.ylabel('Total Emissions', fontsize=12)
plt.xticks(rotation=90)  # Rotate the region names for better readability
plt.grid(axis='y')

# Show the plot
plt.tight_layout()  # Adjust layout to fit labels
plt.show()

# Filter the data for the "West Midlands" Region/Country
west_midlands_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'West Midlands']
west_midlands_data

# Filter out rows where "Second Tier Authority" ends with "Total"
west_midlands_data_filtered = west_midlands_data[~west_midlands_data['Second Tier Authority'].str.endswith("Total")]

west_midlands_data_filtered

# Pivot table creation
pivot_table = pd.pivot_table(
    west_midlands_data_filtered, 
    values=[
        'Commercial Total', 'Waste Management Total', 'Agriculture Total', 
        'Transport Total', 'Domestic Total', 'Public Sector Total', 
        'Industry Total', 'LULUCF Net Emissions','Emissions per km2 (kt CO2e)'
    ],
    index=['Calendar Year'], 
    aggfunc=sum
)

# Transpose the pivot table to have categories as rows and years as columns
pivot_table_transposed = pivot_table.T

# Print the transposed pivot table
# print(pivot_table_transposed)
# pivot_table_transposed.to_csv('features_by_years.csv')

pivot_table

pivot_table_transposed

pivot_table_transposed.corr()

pivot_table.corr()

# Generate the correlation matrix
corr = pivot_table.corr()

# Set up the matplotlib figure
plt.figure(figsize=(12, 10))

# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, annot=True, fmt=".2f", cmap='coolwarm', square=True, linewidths=.5, cbar_kws={"shrink": .5})

# Add title
plt.title('West Midland Correlation Matrix Heatmap')

# Show the plot
plt.show()

# List of columns to keep
columns_to_keep = ['Region/Country', 'Second Tier Authority', 'Local Authority', 'Local Authority Code', 'Calendar Year', 'LULUCF Net Emissions']
# Add columns that end with "Total"
columns_to_keep.extend([col for col in west_midlands_data_filtered.columns if col.endswith("Total")])

west_midlands_filtered_columns = west_midlands_data_filtered[columns_to_keep]

west_midlands_filtered_columns.to_csv('west_midlands_filtered_columns.csv')
west_midlands_filtered_columns

# Create the pivot table of west_midlands local authorities emission
west_midlands_local_authority_emissions = pd.pivot_table(west_midlands_filtered_columns, values='Grand Total', index=['Local Authority'], columns=['Calendar Year'])

west_midlands_local_authority_emissions

import pandas as pd

# Calculate the yearly total emissions for each year
yearly_totals = west_midlands_local_authority_emissions.sum()

# Append the totals as a new row to the DataFrame
west_midlands_local_authority_emissions.loc['Total'] = yearly_totals

west_midlands_local_authority_emissions

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler

data_transposed = west_midlands_local_authority_emissions

data_transposed

X = data_transposed.loc[:, 2005:2020]
y = data_transposed[2021]

test_size = 0.2  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=10)

# Train the model
regressor = LinearRegression()  
regressor.fit(X_train, y_train)

# Evaluate the model
print("Intercept:", regressor.intercept_)
print("Coefficients:", regressor.coef_)

# Predicting
y_pred = regressor.predict(X_test)

# Printing shapes of predictions and test labels
print(y_pred.shape, y_test.shape)

Intercept: -16.304564086553683
Coefficients: [-0.164813    0.01567557  0.30325461 -0.30009949  0.24781068  0.32482985
 -0.01417399 -0.26335244 -0.28176354  0.05105317 -0.09310551  0.08112285
  0.73103766  0.12332474 -0.58931361  0.84632316]
(6,) (6,)

X

y

Local Authority
Birmingham                  4480.654084
Bromsgrove                   711.760256
Cannock Chase                364.042820
Coventry                    1344.000349
Dudley                      1163.033149
East Staffordshire           853.521292
Herefordshire, County of    1472.865799
Lichfield                    754.890754
Malvern Hills                601.444378
Newcastle-under-Lyme         844.190037
North Warwickshire           919.630410
Nuneaton and Bedworth        540.854821
Redditch                     357.787785
Rugby                       2026.454350
Sandwell                    1401.088684
Shropshire                  2773.370585
Solihull                    1226.181599
South Staffordshire          954.939263
Stafford                    1166.734395
Staffordshire Moorlands     1332.991944
Stoke-on-Trent              1127.356084
Stratford-on-Avon           1120.139928
Tamworth                     268.838288
Telford and Wrekin          1002.876199
Walsall                     1088.510816
Warwick                      892.125608
Wolverhampton                985.990590
Worcester                    375.993686
Wychavon                    1148.046126
Wyre Forest                  436.541231
Name: 2021, dtype: float64

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler

west_midlands_local_authority_emissions

X = west_midlands_local_authority_emissions.loc[:, 2005:2020]
y = west_midlands_local_authority_emissions[2021]

test_size = 0.2  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=10)

# Train the model
regressor = LinearRegression()  
regressor.fit(X_train, y_train)

# Evaluate the model
print("Intercept:", regressor.intercept_)
print("Coefficients:", regressor.coef_)

#predicting the test set result
y_pred = regressor.predict(X_test)
print(y_pred.shape,y_test.shape)
y_pred,y_test

Intercept: -16.304564086553683
Coefficients: [-0.164813    0.01567557  0.30325461 -0.30009949  0.24781068  0.32482985
 -0.01417399 -0.26335244 -0.28176354  0.05105317 -0.09310551  0.08112285
  0.73103766  0.12332474 -0.58931361  0.84632316]
(6,) (6,)

(array([1154.20076885,  739.54101022,  873.7407262 ,  390.29534116,
        1351.3682755 , 1136.4148487 ]),
 Local Authority
 Stoke-on-Trent        1127.356084
 Lichfield              754.890754
 East Staffordshire     853.521292
 Cannock Chase          364.042820
 Coventry              1344.000349
 Stratford-on-Avon     1120.139928
 Name: 2021, dtype: float64)

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

# Calculate Mean Absolute Error
mae = mean_absolute_error(y_test, y_pred)

# Calculate Mean Squared Error
mse = mean_squared_error(y_test, y_pred)

# Calculate R^2 Score
r2 = r2_score(y_test, y_pred)

# Calculating the Root Mean Squared Error (RMSE)
rmse = np.sqrt(mse)

print("Mean Absolute Error:", mae)
print("Mean Squared Error:", mse)
print("R-squared Score:", r2)
print("Root Mean Squared Error:", rmse)

Mean Absolute Error: 18.71820519177972
Mean Squared Error: 395.57191898109664
R-squared Score: 0.996065760110844
Root Mean Squared Error: 19.88898989343342

import numpy as np
from sklearn.metrics import mean_squared_error

n = len(y_train)  # number of observations
k = regressor.coef_.shape[0] + 1  # number of parameters (including the intercept)
mse = mean_squared_error(y_test, y_pred)
residuals = y_test - y_pred
sse = np.sum(residuals**2)

# Log-likelihood
sigma_squared = mse
log_likelihood = -0.5 * n * np.log(2 * np.pi) - 0.5 * n * np.log(sigma_squared) - (1 / (2 * sigma_squared)) * sse

# AIC and BIC
aic = 2 * k - 2 * log_likelihood
bic = np.log(n) * k - 2 * log_likelihood

print(f"Log-likelihood: {log_likelihood}")
print(f"AIC: {aic}")
print(f"BIC: {bic}")

Log-likelihood: -96.8185161632904
AIC: 227.6370323265808
BIC: 247.66394744249587

from joblib import dump, load

# Save the model to a file
dump(regressor, 'regression_model.joblib')

['regression_model.joblib']

# Load the model from the file
model = load('regression_model.joblib')

#using model to make predictions
predictions = model.predict(X_test)
predictions

array([1154.20076885,  739.54101022,  873.7407262 ,  390.29534116,
       1351.3682755 , 1136.4148487 ])

from sklearn.linear_model import Ridge

# alpha is the regularization strength
ridge_regressor = Ridge(alpha=1.0)
ridge_regressor.fit(X_train, y_train)

# Evaluate the model
print("Ridge Intercept:", ridge_regressor.intercept_)
print("Ridge Coefficients:", ridge_regressor.coef_)

Ridge Intercept: -16.31947799472755
Ridge Coefficients: [-0.16475797  0.01509699  0.30314981 -0.29964514  0.24736677  0.32550167
 -0.01433364 -0.26439064 -0.27966232  0.05076292 -0.09322371  0.08056498
  0.72973804  0.12303375 -0.58689111  0.84544592]

from sklearn.linear_model import Ridge

# alpha is the regularization strength
ridge_regressor = Ridge(alpha=100.0)
ridge_regressor.fit(X_train, y_train)

# Evaluate the model
print("Ridge Intercept:", ridge_regressor.intercept_)
print("Ridge Coefficients:", ridge_regressor.coef_)

Ridge Intercept: -16.6619146624912
Ridge Coefficients: [-0.16608111 -0.00764947  0.28522153 -0.25679853  0.21426021  0.35359872
 -0.01749576 -0.32572873 -0.15329251  0.03704983 -0.10297709  0.0527268
  0.64133955  0.08796357 -0.41705216  0.78943365]

from sklearn.linear_model import Lasso

# alpha is the regularization strength
# LASSO - least Absolute shrinkage and selection operator
lasso_regressor = Lasso(alpha=0.1)
lasso_regressor.fit(X_train, y_train)

# Evaluate the model
print("Lasso Intercept:", lasso_regressor.intercept_)
print("Lasso Coefficients:", lasso_regressor.coef_)

Lasso Intercept: -20.365287808106586
Lasso Coefficients: [-0.12890393 -0.04281725  0.17837908  0.07241432  0.05918155 -0.09031056
 -0.05182621 -0.20454203  0.13725205  0.18819251 -0.13577117  0.3374395
  0.11786835 -0.0764537   0.19038713  0.45120508]

C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.890e+03, tolerance: 1.837e+03
  model = cd_fast.enet_coordinate_descent(

from sklearn.linear_model import ElasticNet

# alpha is the regularization strength and l1_ratio controls the ratio of L1 to L2 penalty
elasticnet_regressor = ElasticNet(alpha=0.1, l1_ratio=0.5)
elasticnet_regressor.fit(X_train, y_train)

# Evaluate the model
print("ElasticNet Intercept:", elasticnet_regressor.intercept_)
print("ElasticNet Coefficients:", elasticnet_regressor.coef_)

ElasticNet Intercept: -20.36437515103262
ElasticNet Coefficients: [-0.12891403 -0.04279652  0.17837821  0.07241618  0.05919524 -0.09035966
 -0.05182048 -0.20454239  0.13730612  0.18817715 -0.13582386  0.3374735
  0.11786115 -0.07647005  0.19042191  0.45119084]

C:\ProgramData\anaconda3\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.888e+03, tolerance: 1.837e+03
  model = cd_fast.enet_coordinate_descent(

from sklearn.linear_model import RidgeCV

# Define a set of alpha values to test
alphas = [0.1, 1.0, 10.0, 100.0]

# Create the RidgeCV regressor
ridge_cv = RidgeCV(alphas=alphas, store_cv_values=True)

# Fit the model to the training data
ridge_cv.fit(X_train, y_train)

# Retrieve the best alpha and coefficients
best_alpha = ridge_cv.alpha_
print("Best Alpha:", best_alpha)
print("RidgeCV Intercept:", ridge_cv.intercept_)
print("RidgeCV Coefficients:", ridge_cv.coef_)

#Inspecting the mean squared errors for each alpha
cv_mse = np.mean(ridge_cv.cv_values_, axis=0)
print("Mean Squared Errors for each alpha:", cv_mse)

Best Alpha: 100.0
RidgeCV Intercept: -16.66191276546465
RidgeCV Coefficients: [-0.16608111 -0.00764947  0.28522153 -0.25679853  0.21426021  0.35359872
 -0.01749576 -0.32572873 -0.15329251  0.03704983 -0.10297709  0.0527268
  0.64133955  0.08796357 -0.41705216  0.78943365]
Mean Squared Errors for each alpha: [1251.71611458 1244.52879052 1180.01026315  864.35242285]

from sklearn.metrics import mean_squared_error, r2_score

# Predict on the test set
y_test_pred = ridge_cv.predict(X_test)

# Calculate metrics
r2 = r2_score(y_test, y_test_pred)
mse = mean_squared_error(y_test, y_test_pred)
rmse = np.sqrt(mse)

print(f"R-squared Score: {r2}")
print(f"Mean Squared Error: {mse}")
print(f"Root Mean Squared Error: {rmse}")

R-squared Score: 0.9971848346207
Mean Squared Error: 283.0535002220636
Root Mean Squared Error: 16.824193895163702

# Define a set of alpha values to test
alphas = [0.1, 1.0, 10.0, 100.0]

# Create the RidgeCV regressor
ridge_cv = RidgeCV(alphas=alphas, scoring='neg_mean_absolute_error', store_cv_values=True)

# Fit the model to the training data
ridge_cv.fit(X_train, y_train)

# Retrieve the best alpha and coefficients
best_alpha = ridge_cv.alpha_
print("Best Alpha:", best_alpha)

Best Alpha: 100.0

import matplotlib.pyplot as plt
import pandas as pd

yearly_totals = west_midlands_local_authority_emissions.sum()

plt.figure(figsize=(10, 6))
yearly_totals.plot(kind='line', marker='o')

plt.title('Yearly Total Emissions in West Midlands')
plt.xlabel('Year')
plt.ylabel('Total Emissions')
plt.grid(True)

plt.show()

# Filter the data for the "london" Region/Country
London_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'London']
London_data

# Filter out rows where "Second Tier Authority" ends with "Total"
London_data_filtered = London_data[~London_data['Second Tier Authority'].str.endswith("Total")]

# List of columns to keep
columns_to_keep = ['Region/Country', 'Second Tier Authority', 'Local Authority', 'Local Authority Code', 'Calendar Year', 'LULUCF Net Emissions']
# Add columns that end with "Total"
columns_to_keep.extend([col for col in London_data_filtered.columns if col.endswith("Total")])

London_filtered_columns = London_data_filtered[columns_to_keep]

London_filtered_columns.to_csv('London_filtered_columns.csv')
London_filtered_columns

# Create the pivot table of west_midlands local authorities emission
London_local_authority_emissions = pd.pivot_table(London_filtered_columns, values='Grand Total', index=['Local Authority'], columns=['Calendar Year'])

London_local_authority_emissions

import pandas as pd

# Calculate the yearly total emissions for each year
yearly_totals = London_local_authority_emissions.sum()

# Append the totals as a new row to the DataFrame
London_local_authority_emissions.loc['Total'] = yearly_totals

London_local_authority_emissions

import matplotlib.pyplot as plt
import pandas as pd

yearly_totals = London_local_authority_emissions.sum()

plt.figure(figsize=(10, 6))
yearly_totals.plot(kind='line', marker='o')

plt.title('Yearly Total Emissions in London')
plt.xlabel('Year')
plt.ylabel('Total Emissions')
plt.grid(True)

plt.show()

# Filter the data for the "Wales" Region/Country
Wales_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'Wales']
Wales_data

# Filter out rows where "Second Tier Authority" ends with "Total"
Wales_data_filtered = Wales_data[~Wales_data['Second Tier Authority'].str.endswith("Total")]

# List of columns to keep
columns_to_keep = ['Region/Country', 'Second Tier Authority', 'Local Authority', 'Local Authority Code', 'Calendar Year', 'LULUCF Net Emissions']
# Add columns that end with "Total"
columns_to_keep.extend([col for col in Wales_data_filtered.columns if col.endswith("Total")])

Wales_filtered_columns = Wales_data_filtered[columns_to_keep]

Wales_filtered_columns.to_csv('Wales_filtered_columns.csv')
Wales_filtered_columns

# Create the pivot table of west_midlands local authorities emission
Wales_local_authority_emissions = pd.pivot_table(Wales_filtered_columns, values='Grand Total', index=['Local Authority'], columns=['Calendar Year'])

Wales_local_authority_emissions

# Calculate the yearly total emissions for each year
yearly_totals = Wales_local_authority_emissions.sum()

# Append the totals as a new row to the DataFrame
Wales_local_authority_emissions.loc['Total'] = yearly_totals

Wales_local_authority_emissions

import matplotlib.pyplot as plt
import pandas as pd

yearly_totals = Wales_local_authority_emissions.sum()

plt.figure(figsize=(10, 6))
yearly_totals.plot(kind='line', marker='o')

plt.title('Yearly Total Emissions in Wales')
plt.xlabel('Year')
plt.ylabel('Total Emissions')
plt.grid(True)

plt.show()

yearly_totals

Calendar Year
2005    44547.469945
2006    44645.233337
2007    43570.406943
2008    41629.979644
2009    36926.734158
2010    39750.886937
2011    35500.486005
2012    34837.811399
2013    37250.523547
2014    35750.830428
2015    33984.992931
2016    32164.691926
2017    31837.818836
2018    30678.326243
2019    30345.144827
2020    27768.270278
2021    29243.325974
dtype: float64

# Compute average per capita emissions
data_1_1_actual['Per Capita Emissions'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce') / \
                                          pd.to_numeric(data_1_1_actual["Population ('000s, mid-year estimate)"], errors='coerce')

# Compute average emissions per km^2
data_1_1_actual['Emissions per km^2'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce') / \
                                        pd.to_numeric(data_1_1_actual['Area (km2)'], errors='coerce')

# Group by 'Region/Country' to get average per capita emissions and emissions per km^2 for each region
average_emissions = data_1_1_actual.groupby('Region/Country')[['Per Capita Emissions', 'Emissions per km^2']].mean()

average_emissions

import pandas as pd
import numpy as np
from sklearn.manifold import TSNE
import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset
data_transposed = west_midlands_local_authority_emissions

# Select numerical columns and exclude non-numerical data
numerical_data = data_transposed.select_dtypes(include=[np.number])

# Handling missing values by filling them with the mean of each column
numerical_data_filled = numerical_data.fillna(numerical_data.mean())

# Sampling a subset of the data to make the computation more manageable
sampled_data = numerical_data_filled.sample(frac=0.01, random_state=0)  # Adjust 'frac' to change the sample size

# t-SNE transformation
tsne = TSNE(n_components=2, random_state=0)
tsne_results = tsne.fit_transform(sampled_data)

# Creating a DataFrame for the t-SNE results
tsne_df = pd.DataFrame({
    'TSNE1': tsne_results[:, 0],
    'TSNE2': tsne_results[:, 1]
})

# Plotting the t-SNE results
plt.figure(figsize=(12, 8))
sns.scatterplot(x='TSNE1', y='TSNE2', color='blue', edgecolor='black', data=tsne_df)
plt.title('t-SNE visualization of sampled data')
plt.xlabel('TSNE Component 1')
plt.ylabel('TSNE Component 2')
plt.grid(True)
plt.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[112], line 27
     25 # t-SNE transformation
     26 tsne = TSNE(n_components=2, random_state=0)
---> 27 tsne_results = tsne.fit_transform(sampled_data)
     29 # Creating a DataFrame for the t-SNE results
     30 tsne_df = pd.DataFrame({
     31     'TSNE1': tsne_results[:, 0],
     32     'TSNE2': tsne_results[:, 1]
     33 })

File C:\ProgramData\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:1118, in TSNE.fit_transform(self, X, y)
   1097 """Fit X into an embedded space and return that transformed output.
   1098 
   1099 Parameters
   (...)
   1115     Embedding of the training data in low-dimensional space.
   1116 """
   1117 self._validate_params()
-> 1118 self._check_params_vs_input(X)
   1119 embedding = self._fit(X)
   1120 self.embedding_ = embedding

File C:\ProgramData\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:829, in TSNE._check_params_vs_input(self, X)
    827 def _check_params_vs_input(self, X):
    828     if self.perplexity >= X.shape[0]:
--> 829         raise ValueError("perplexity must be less than n_samples")

ValueError: perplexity must be less than n_samples

data_transposed

data_transposed.dtypes

Calendar Year
2005    float64
2006    float64
2007    float64
2008    float64
2009    float64
2010    float64
2011    float64
2012    float64
2013    float64
2014    float64
2015    float64
2016    float64
2017    float64
2018    float64
2019    float64
2020    float64
2021    float64
dtype: object

scatter

matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, *, edgecolors=None, plotnonfinite=False, data=None, **kwargs)

import matplotlib.pypl

matplotlib.pyplot.scatter(data_transposed)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[117], line 1
----> 1 matplotlib.pyplot.scatter(data_transposed)

NameError: name 'matplotlib' is not defined

import pandas as pd
import numpy as np
from sklearn.manifold import TSNE
import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset



# Select numerical columns and exclude non-numerical data
numerical_data = data.select_dtypes(include=[np.number])

# Handling missing values by filling them with the mean of each column
numerical_data_filled = numerical_data.fillna(numerical_data.mean())

# Sampling a subset of the data to make the computation more manageable
sampled_data = numerical_data_filled.sample(frac=0.1, random_state=0)  # Adjust 'frac' to change the sample size

# t-SNE transformation
tsne = TSNE(n_components=2, random_state=0)
tsne_results = tsne.fit_transform(sampled_data)

# Creating a DataFrame for the t-SNE results
tsne_df = pd.DataFrame({
    'TSNE1': tsne_results[:, 0],
    'TSNE2': tsne_results[:, 1]
})

# Plotting the t-SNE results
plt.figure(figsize=(12, 8))
sns.scatterplot(x='TSNE1', y='TSNE2', color='blue', edgecolor='black', data=tsne_df)
plt.title('t-SNE visualization of sampled data')
plt.xlabel('TSNE Component 1')
plt.ylabel('TSNE Component 2')
plt.grid(True)
plt.show()

numerical_data

tsne_df

import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns


tsne_results = tsne_df

# Extract the t-SNE components
tsne_component_1 = tsne_results['TSNE1']
tsne_component_2 = tsne_results['TSNE2']

# Perform KMeans clustering
# The number of clusters is set to 5 
kmeans = KMeans(n_clusters=5, random_state=0)
tsne_results['Cluster'] = kmeans.fit_predict(tsne_results[['TSNE1', 'TSNE2']])

# Plotting the t-SNE results with different colors for each cluster
plt.figure(figsize=(12, 8))
sns.scatterplot(x='TSNE1', y='TSNE2', hue='Cluster', palette='viridis', data=tsne_results, legend='full')

# Title and labels
plt.title('t-SNE visualization with Cluster Colors')
plt.xlabel('TSNE Component 1')
plt.ylabel('TSNE Component 2')
plt.legend(title='Cluster')
plt.grid(True)
plt.show()

C:\ProgramData\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
C:\ProgramData\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.
  warnings.warn(

tsne_df

tsne_df.to_csv('tsne.csv', index=False)

# Sort the regions based on average per capita emissions
sorted_per_capita = average_emissions['Per Capita Emissions'].sort_values(ascending=False)

# Plotting average per capita emissions for each region
plt.figure(figsize=(14, 10))
sorted_per_capita.plot(kind='bar', color='lightblue')
plt.title('Average Per Capita Emissions by Region')
plt.ylabel('Average Per Capita Emissions (kt CO2e)')
plt.xlabel('Region/Country')
plt.xticks(rotation=90)
plt.grid(axis='y')
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import pandas as pd

# Filter out rows where 'Region/Country' ends with 'Total'
filtered_average_emissions = average_emissions[~average_emissions['Region/Country'].str.endswith('Total')]

# Sort the regions based on average per capita emissions
sorted_per_capita = filtered_average_emissions['Per Capita Emissions'].sort_values(ascending=False)

# Plotting average per capita emissions for each region
plt.figure(figsize=(14, 10))
sorted_per_capita.plot(kind='bar', color='lightblue')
plt.title('Average Per Capita Emissions by Region (Excluding Totals)')
plt.ylabel('Average Per Capita Emissions (kt CO2e)')
plt.xlabel('Region/Country')
plt.xticks(rotation=90)
plt.grid(axis='y')
plt.tight_layout()
plt.show()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File C:\ProgramData\anaconda3\lib\site-packages\pandas\core\indexes\base.py:3802, in Index.get_loc(self, key, method, tolerance)
   3801 try:
-> 3802     return self._engine.get_loc(casted_key)
   3803 except KeyError as err:

File C:\ProgramData\anaconda3\lib\site-packages\pandas\_libs\index.pyx:138, in pandas._libs.index.IndexEngine.get_loc()

File C:\ProgramData\anaconda3\lib\site-packages\pandas\_libs\index.pyx:165, in pandas._libs.index.IndexEngine.get_loc()

File pandas\_libs\hashtable_class_helper.pxi:5745, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas\_libs\hashtable_class_helper.pxi:5753, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Region/Country'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[82], line 5
      2 import pandas as pd
      4 # Filter out rows where 'Region/Country' ends with 'Total'
----> 5 filtered_average_emissions = average_emissions[~average_emissions['Region/Country'].str.endswith('Total')]
      7 # Sort the regions based on average per capita emissions
      8 sorted_per_capita = filtered_average_emissions['Per Capita Emissions'].sort_values(ascending=False)

File C:\ProgramData\anaconda3\lib\site-packages\pandas\core\frame.py:3807, in DataFrame.__getitem__(self, key)
   3805 if self.columns.nlevels > 1:
   3806     return self._getitem_multilevel(key)
-> 3807 indexer = self.columns.get_loc(key)
   3808 if is_integer(indexer):
   3809     indexer = [indexer]

File C:\ProgramData\anaconda3\lib\site-packages\pandas\core\indexes\base.py:3804, in Index.get_loc(self, key, method, tolerance)
   3802     return self._engine.get_loc(casted_key)
   3803 except KeyError as err:
-> 3804     raise KeyError(key) from err
   3805 except TypeError:
   3806     # If we have a listlike key, _check_indexing_error will raise
   3807     #  InvalidIndexError. Otherwise we fall through and re-raise
   3808     #  the TypeError.
   3809     self._check_indexing_error(key)

KeyError: 'Region/Country'

# Sort the regions based on average emissions per km^2
sorted_per_km2 = average_emissions['Emissions per km^2'].sort_values(ascending=False)

# Plotting average emissions per km^2 for each region
plt.figure(figsize=(14, 10))
sorted_per_km2.plot(kind='bar', color='lightcoral')
plt.title('Average Emissions per km^2 by Region')
plt.ylabel('Average Emissions per km^2 (kt CO2e)')
plt.xlabel('Region/Country')
plt.xticks(rotation=90)
plt.grid(axis='y')
plt.tight_layout()
plt.show()

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Plotting the national total emissions time series
plt.figure(figsize=(12, 6))
national_time_series.plot(marker='o', linestyle='-', color='black')
plt.title('National Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.grid(True)
plt.show()

# Assuming data_1_1_actual is a pandas DataFrame with the relevant data
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']

# Group by 'Calendar Year' and sum the 'Grand Total'
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Convert the Series back to a DataFrame and reset the index
national_time_series_table = national_time_series.reset_index()

# Display the DataFrame as a table
national_time_series_table

national_time_series_table['Grand Total'].mean()

515499.05076104676

sns.histplot(data=national_time_series_table,x='Grand Total',bins=20)

<Axes: xlabel='Grand Total', ylabel='Count'>

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Initialize the linear regression model
model = LinearRegression()

# Reshape the data and fit the model
X = national_time_series_table['Calendar Year'].values.reshape(-1, 1)  # Independent variable
y = national_time_series_table['Grand Total'].values  # Dependent variable
model.fit(X, y)

# Make predictions
national_time_series_table['Predicted Emission'] = model.predict(X)

# Coefficients
slope = model.coef_[0]
intercept = model.intercept_

# R^2 score
r_squared = model.score(X, y)

# Plotting the actual vs predicted values
plt.figure(figsize=(14, 7))
plt.scatter(X, y, color='blue', edgecolors='black', label='Actual Emission')
plt.plot(X, national_time_series_table['Predicted Emission'], color='red', linewidth=2, label='Predicted Emission')
plt.title('National Annual Emissions for All Sectors (2005-2021)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.grid(True)
plt.legend()
plt.show()

(slope, intercept, r_squared)

(-17644.370521085086, 36033616.90970532, 0.9742324674401649)

import numpy as np

# Predicting the emissions for the year 2024
year_2024 = np.array([[2024]])
emissions_2024 = model.predict(year_2024)
emissions_2024[0]

321410.97502910346

# Predicting the emissions for the year 2022
year_2022 = np.array([[2022]])
emissions_forecast_2022 = model.predict(year_2022)

emissions_forecast_2022[0]

356699.71607127786

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Exclude the year 2020 from the dataset for training
training_data = national_time_series_table[national_time_series_table['Calendar Year'] != 2020]

# Initialize the linear regression model
model = LinearRegression()

# Reshape the data and fit the model
X_train = training_data['Calendar Year'].values.reshape(-1, 1)  # Independent variable (excluding 2020)
y_train = training_data['Grand Total'].values  # Dependent variable (excluding 2020)
model.fit(X_train, y_train)

# Make predictions for the entire dataset including 2020
X_full = national_time_series_table['Calendar Year'].values.reshape(-1, 1)
national_time_series_table['Predicted Emission'] = model.predict(X_full)

# Predictions for 2022 to 2024
future_years = np.array([[2022], [2023], [2024]])
future_predictions = model.predict(future_years)

# Coefficients and R^2 score
slope = model.coef_[0]
intercept = model.intercept_
r_squared = model.score(X_train, y_train)

# Plotting the actual vs predicted values
plt.figure(figsize=(14, 7))
plt.scatter(X_train, y_train, color='blue', edgecolors='black', label='Actual Emission (Excluding 2020)')
plt.scatter([2020], national_time_series_table[national_time_series_table['Calendar Year'] == 2020]['Grand Total'], color='orange', edgecolors='black', label='Outlier Data (2020)')
plt.plot(X_full, national_time_series_table['Predicted Emission'], color='red', linewidth=2, label='Predicted Emission')
plt.scatter(future_years, future_predictions, color='green', edgecolors='black', label='Predicted Emission (2022-2024)')
plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.grid(True)
plt.legend()
plt.show()

# Extracting the predicted emissions for 2022, 2023, and 2024
predicted_2022 = future_predictions[0]
predicted_2023 = future_predictions[1]
predicted_2024 = future_predictions[2]

# Printing the predicted emissions
print(f"Predicted Emissions for 2022: {predicted_2022}")
print(f"Predicted Emissions for 2023: {predicted_2023}")
print(f"Predicted Emissions for 2024: {predicted_2024}")

Predicted Emissions for 2022: 360642.15265548974
Predicted Emissions for 2023: 343314.9896756634
Predicted Emissions for 2024: 325987.82669582963

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Calculating MSE, MAE, and R^2 score using the training data (excluding 2020)
mse = mean_squared_error(y_train, model.predict(X_train))
mae = mean_absolute_error(y_train, model.predict(X_train))
r2 = r2_score(y_train, model.predict(X_train))

# Printing the metrics
print(f"Mean Squared Error (MSE): {mse}")
print(f"Mean Absolute Error (MAE): {mae}")
print(f"R-squared (R²) Score: {r2}")

Mean Squared Error (MSE): 192430790.51198345
Mean Absolute Error (MAE): 11517.010197187687
R-squared (R²) Score: 0.9719953551351934

import pandas as pd
import numpy as np
from sklearn.linear_model import SGDRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
import matplotlib.pyplot as plt

# Exclude the year 2020 from the dataset for training
training_data = national_time_series_table[national_time_series_table['Calendar Year'] != 2020]

# Reshape the data
X_train = training_data['Calendar Year'].values.reshape(-1, 1)  # Independent variable (excluding 2020)
y_train = training_data['Grand Total'].values  # Dependent variable (excluding 2020)

# It's important to scale features when using SGD
pipeline = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3))

# Train the SGD model
sgd_model = pipeline.fit(X_train, y_train)

# Make predictions for the entire dataset including 2020
X_full = national_time_series_table['Calendar Year'].values.reshape(-1, 1)
national_time_series_table['Predicted Emission'] = sgd_model.predict(X_full)

# Predictions for 2022 to 2024
future_years = np.array([[2022], [2023], [2024]])
future_predictions = sgd_model.predict(future_years)

# Plotting the actual vs predicted values
plt.figure(figsize=(14, 7))
plt.scatter(X_train, y_train, color='blue', edgecolors='black', label='Actual Emission (Excluding 2020)')
plt.scatter([2020], national_time_series_table[national_time_series_table['Calendar Year'] == 2020]['Grand Total'], color='orange', edgecolors='black', label='Outlier Data (2020)')
plt.plot(X_full, national_time_series_table['Predicted Emission'], color='red', linewidth=2, label='Predicted Emission')
plt.scatter(future_years, future_predictions, color='green', edgecolors='black', label='Predicted Emission (2022-2024)')
plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.grid(True)
plt.legend()
plt.show()

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Calculating the metrics
mse = mean_squared_error(y_train, sgd_model.predict(X_train))
mae = mean_absolute_error(y_train, sgd_model.predict(X_train))
r2 = r2_score(y_train, sgd_model.predict(X_train))

# Printing the metrics
print(f"Mean Squared Error (MSE): {mse}")
print(f"Mean Absolute Error (MAE): {mae}")
print(f"R-squared (R²) Score: {r2}")

Mean Squared Error (MSE): 192431070.8977003
Mean Absolute Error (MAE): 11522.05633117702
R-squared (R²) Score: 0.9719953143303803

pip install statsmodels

Requirement already satisfied: statsmodels in c:\programdata\anaconda3\lib\site-packages (0.13.5)
Requirement already satisfied: numpy>=1.22.3 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (1.23.5)
Requirement already satisfied: packaging>=21.3 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (22.0)
Requirement already satisfied: scipy>=1.3 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (1.10.0)
Requirement already satisfied: patsy>=0.5.2 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (0.5.3)
Requirement already satisfied: pandas>=0.25 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (1.5.3)
Requirement already satisfied: pytz>=2020.1 in c:\programdata\anaconda3\lib\site-packages (from pandas>=0.25->statsmodels) (2022.7)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\programdata\anaconda3\lib\site-packages (from pandas>=0.25->statsmodels) (2.8.2)
Requirement already satisfied: six in c:\programdata\anaconda3\lib\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)
Note: you may need to restart the kernel to use updated packages.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.nonparametric.kernel_regression import KernelReg


# considering using the data excluding the outlier year 2020
training_data = national_time_series_table[national_time_series_table['Calendar Year'] != 2020]

# Prepare the data
X_train = training_data['Calendar Year'].values
y_train = training_data['Grand Total'].values

# Kernel Regression
kr = KernelReg(endog=y_train, exog=X_train, var_type='c')  # 'c' for continuous
X_predict = np.linspace(X_train.min(), X_train.max(), 100)  # Range of years for prediction
y_kr, y_std = kr.fit(X_predict)

# Plotting the actual data and the kernel regression prediction
plt.figure(figsize=(14, 7))
plt.scatter(X_train, y_train, color='blue', label='Actual Emissions (Excluding 2020)')
plt.plot(X_predict, y_kr, color='red', label='Kernel Regression Prediction')
plt.fill_between(X_predict, y_kr - y_std, y_kr + y_std, color='red', alpha=0.2, label='Prediction Standard Error')
plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.legend()
plt.grid(True)
plt.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[98], line 24
     22 plt.scatter(X_train, y_train, color='blue', label='Actual Emissions (Excluding 2020)')
     23 plt.plot(X_predict, y_kr, color='red', label='Kernel Regression Prediction')
---> 24 plt.fill_between(X_predict, y_kr - y_std, y_kr + y_std, color='red', alpha=0.2, label='Prediction Standard Error')
     25 plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
     26 plt.xlabel('Calendar Year')

File C:\ProgramData\anaconda3\lib\site-packages\matplotlib\pyplot.py:2571, in fill_between(x, y1, y2, where, interpolate, step, data, **kwargs)
   2567 @_copy_docstring_and_deprecators(Axes.fill_between)
   2568 def fill_between(
   2569         x, y1, y2=0, where=None, interpolate=False, step=None, *,
   2570         data=None, **kwargs):
-> 2571     return gca().fill_between(
   2572         x, y1, y2=y2, where=where, interpolate=interpolate, step=step,
   2573         **({"data": data} if data is not None else {}), **kwargs)

File C:\ProgramData\anaconda3\lib\site-packages\matplotlib\__init__.py:1442, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)
   1439 @functools.wraps(func)
   1440 def inner(ax, *args, data=None, **kwargs):
   1441     if data is None:
-> 1442         return func(ax, *map(sanitize_sequence, args), **kwargs)
   1444     bound = new_sig.bind(ax, *args, **kwargs)
   1445     auto_label = (bound.arguments.get(label_namer)
   1446                   or bound.kwargs.get(label_namer))

File C:\ProgramData\anaconda3\lib\site-packages\matplotlib\axes\_axes.py:5431, in Axes.fill_between(self, x, y1, y2, where, interpolate, step, **kwargs)
   5429 def fill_between(self, x, y1, y2=0, where=None, interpolate=False,
   5430                  step=None, **kwargs):
-> 5431     return self._fill_between_x_or_y(
   5432         "x", x, y1, y2,
   5433         where=where, interpolate=interpolate, step=step, **kwargs)

File C:\ProgramData\anaconda3\lib\site-packages\matplotlib\axes\_axes.py:5343, in Axes._fill_between_x_or_y(self, ind_dir, ind, dep1, dep2, where, interpolate, step, **kwargs)
   5340 for name, array in [
   5341         (ind_dir, ind), (f"{dep_dir}1", dep1), (f"{dep_dir}2", dep2)]:
   5342     if array.ndim > 1:
-> 5343         raise ValueError(f"{name!r} is not 1-dimensional")
   5345 if where is None:
   5346     where = True

ValueError: 'y1' is not 1-dimensional

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.nonparametric.kernel_regression import KernelReg


# consider using the data excluding the outlier year 2020
training_data = national_time_series_table[national_time_series_table['Calendar Year'] != 2020]

# Prepare the data
X_train = training_data['Calendar Year'].values
y_train = training_data['Grand Total'].values

# Kernel Regression
kr = KernelReg(endog=y_train, exog=X_train, var_type='c')  # 'c' for continuous
X_predict = np.linspace(X_train.min(), X_train.max(), 100)  # Range of years for prediction
y_kr, y_std = kr.fit(X_predict)

# Ensure that y_std is 1-dimensional
if y_std.ndim > 1:
    y_std = y_std[:,0]

# Plotting the actual data and the kernel regression prediction
plt.figure(figsize=(14, 7))
plt.scatter(X_train, y_train, color='blue', label='Actual Emissions (Excluding 2020)')
plt.plot(X_predict, y_kr, color='red', label='Kernel Regression Prediction')
plt.fill_between(X_predict, y_kr - y_std, y_kr + y_std, color='red', alpha=0.2, label='Prediction Standard Error')
plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.legend()
plt.grid(True)
plt.show()

from sklearn.metrics import mean_squared_error, mean_absolute_error

# Predictions on the training data
y_pred_train, _ = kr.fit(X_train)

# Calculating MSE and MAE
mse = mean_squared_error(y_train, y_pred_train)
mae = mean_absolute_error(y_train, y_pred_train)

# Printing the metrics
print(f"Mean Squared Error (MSE): {mse}")
print(f"Mean Absolute Error (MAE): {mae}")

# R-squared is not typically used with non-parametric models like kernel regression

Mean Squared Error (MSE): 98854665.32507062
Mean Absolute Error (MAE): 7560.483432559933

from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
import numpy as np

# Define a range of bandwidths to test
bandwidths = np.linspace(0.1, 2, 20)

# Best bandwidth and score initialization
best_bandwidth = None
best_score = float('inf')

# K-Fold for cross-validation
kf = KFold(n_splits=5)

for bandwidth in bandwidths:
    mse_scores = []
    for train_index, test_index in kf.split(X_train):
        X_train_kf, X_test_kf = X_train[train_index], X_train[test_index]
        y_train_kf, y_test_kf = y_train[train_index], y_train[test_index]

        # Kernel Regression with the current bandwidth
        kr = KernelReg(endog=y_train_kf, exog=X_train_kf, var_type='c', bw=[bandwidth])
        y_pred_kf, _ = kr.fit(X_test_kf)

        # Calculate MSE for this fold
        mse = mean_squared_error(y_test_kf, y_pred_kf)
        mse_scores.append(mse)

    # Average MSE score for this bandwidth
    avg_mse = np.mean(mse_scores)
    if avg_mse < best_score:
        best_score = avg_mse
        best_bandwidth = bandwidth

# Best bandwidth
print(f"Best Bandwidth: {best_bandwidth}")

Best Bandwidth: 2.0

# Fitting the Kernel Regression model with the best bandwidth
kr_optimal = KernelReg(endog=y_train, exog=X_train, var_type='c', bw=[best_bandwidth])
y_pred_optimal, _ = kr_optimal.fit(X_train)

# Plotting
plt.figure(figsize=(14, 7))
plt.scatter(X_train, y_train, color='blue', label='Actual Emissions (Excluding 2020)')
plt.plot(X_train, y_pred_optimal, color='red', label='Kernel Regression Prediction (Optimal Bandwidth)')
plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.legend()
plt.grid(True)
plt.show()

from sklearn.metrics import mean_squared_error, mean_absolute_error

# Making predictions on the training data using the optimal bandwidth model
y_pred_optimal, _ = kr_optimal.fit(X_train)

# Calculating MSE and MAE
mse_optimal = mean_squared_error(y_train, y_pred_optimal)
mae_optimal = mean_absolute_error(y_train, y_pred_optimal)

# Printing the metrics
print(f"Mean Squared Error (MSE): {mse_optimal}")
print(f"Mean Absolute Error (MAE): {mae_optimal}")

Mean Squared Error (MSE): 122452257.76925212
Mean Absolute Error (MAE): 8541.34711829967

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.nonparametric.kernel_regression import KernelReg
from sklearn.metrics import mean_squared_error, mean_absolute_error

# Creating a sample DataFrame based on the data
data = {
    'Calendar Year': [2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2021],
    'Grand Total': [
        656867, 649603, 634904, 614046, 558455, 571129, 525842, 544172, 531229, 491490, 475077, 449383, 437824, 430745, 416856, 399046
    ]
}
national_time_series_table = pd.DataFrame(data)

# Exclude the year 2020 from the dataset for training
training_data = national_time_series_table

# Prepare the data
X_train = training_data['Calendar Year'].values
y_train = training_data['Grand Total'].values

# Best bandwidth (as obtained from previous grid search)
best_bandwidth = 2.0

# Kernel Regression with the optimal bandwidth
kr_optimal = KernelReg(endog=y_train, exog=X_train, var_type='c', bw=[best_bandwidth])
y_pred_optimal, _ = kr_optimal.fit(X_train)

# Calculating MSE and MAE
mse_optimal = mean_squared_error(y_train, y_pred_optimal)
mae_optimal = mean_absolute_error(y_train, y_pred_optimal)

# Predicting emissions for 2022, 2023, and 2024
future_years = np.array([2022, 2023, 2024]).reshape(-1, 1)
future_predictions, _ = kr_optimal.fit(future_years)

# Printing the metrics
print(f"Mean Squared Error (MSE): {mse_optimal}")
print(f"Mean Absolute Error (MAE): {mae_optimal}")

# Plotting the actual data, kernel regression prediction, and future predictions
plt.figure(figsize=(14, 7))
plt.scatter(X_train, y_train, color='blue', label='Actual Emissions (Excluding 2020)')
plt.plot(X_train, y_pred_optimal, color='red', label='Kernel Regression Prediction (Optimal Bandwidth)')
plt.scatter(future_years, future_predictions, color='green', marker='o', label='Predicted Emissions (2022-2024)')
plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.legend()
plt.grid(True)
plt.show()

# Print predicted emissions for 2022, 2023, and 2024
for year, prediction in zip(range(2022, 2025), future_predictions):
    print(f"Predicted Emissions for {year}: {prediction}")

Mean Squared Error (MSE): 122454007.79715742
Mean Absolute Error (MAE): 8541.433550188427

Predicted Emissions for 2022: 388856.10498873994
Predicted Emissions for 2023: 379455.74041452305
Predicted Emissions for 2024: 370225.2050343985

# Plotting the actual data, kernel regression prediction, and future predictions
plt.figure(figsize=(14, 7))
plt.scatter(X_train, y_train, color='blue', label='Actual Emissions (Excluding 2020)')
plt.plot(X_train, y_pred_optimal, color='red', label='Kernel Regression Prediction (Optimal Bandwidth)')
plt.scatter(future_years, future_predictions, color='green', marker='o', label='Predicted Emissions (2022-2024)')

# Adjust x-axis to show each year
plt.xticks(np.arange(min(X_train), max(future_years) + 1, 1.0))

plt.title('National Annual Emissions for All Sectors (2005-2021, excluding 2020)')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.legend()
plt.grid(True)
plt.show()

# Print predicted emissions for 2022, 2023, and 2024
for year, prediction in zip(range(2022, 2025), future_predictions):
    print(f"Predicted Emissions for {year}: {prediction}")

Predicted Emissions for 2022: 388856.10498873994
Predicted Emissions for 2023: 379455.74041452305
Predicted Emissions for 2024: 370225.2050343985

from sklearn.kernel_ridge import KernelRidge

# Prepare the data (assuming the 'national_time_series_table' DataFrame is provided with correct columns)
# X = national_time_series_table['Calendar Year'].values.reshape(-1, 1)  # Independent variable
# y = national_time_series_table['Grand Total'].values  # Dependent variable

X = national_time_series_table['Calendar Year'].values.reshape(-1, 1)  # Independent variable from the user's data
y = national_time_series_table['Total Emission'].values  # Dependent variable from the user's data

# Initialize Kernel Ridge Regression model with a radial basis function (RBF) kernel, also known as Gaussian kernel
kernel_model = KernelRidge(kernel='rbf')

# Fit the model to the data
kernel_model.fit(X, y)

# Predict using the model
national_time_series_table['Predicted Emission Kernel'] = kernel_model.predict(X)

# Plot the original data and the kernel regression fit
plt.figure(figsize=(14, 7))
plt.scatter(X, y, color='black', label='Actual Emission')
plt.plot(X, df['Predicted Emission Kernel'], color='green', linestyle='--', linewidth=2, label='Kernel Regression')
plt.title('West Midlands Region Annual Emissions for All Sectors (2005-2021) - Kernel Regression Fit')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.legend()
plt.show()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File C:\ProgramData\anaconda3\lib\site-packages\pandas\core\indexes\base.py:3802, in Index.get_loc(self, key, method, tolerance)
   3801 try:
-> 3802     return self._engine.get_loc(casted_key)
   3803 except KeyError as err:

File C:\ProgramData\anaconda3\lib\site-packages\pandas\_libs\index.pyx:138, in pandas._libs.index.IndexEngine.get_loc()

File C:\ProgramData\anaconda3\lib\site-packages\pandas\_libs\index.pyx:165, in pandas._libs.index.IndexEngine.get_loc()

File pandas\_libs\hashtable_class_helper.pxi:5745, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas\_libs\hashtable_class_helper.pxi:5753, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Total Emission'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[107], line 9
      3 # Prepare the data (assuming the 'national_time_series_table' DataFrame is provided with correct columns)
      4 # X = national_time_series_table['Calendar Year'].values.reshape(-1, 1)  # Independent variable
      5 # y = national_time_series_table['Grand Total'].values  # Dependent variable
      8 X = national_time_series_table['Calendar Year'].values.reshape(-1, 1)  # Independent variable from the user's data
----> 9 y = national_time_series_table['Total Emission'].values  # Dependent variable from the user's data
     11 # Initialize Kernel Ridge Regression model with a radial basis function (RBF) kernel, also known as Gaussian kernel
     12 kernel_model = KernelRidge(kernel='rbf')

File C:\ProgramData\anaconda3\lib\site-packages\pandas\core\frame.py:3807, in DataFrame.__getitem__(self, key)
   3805 if self.columns.nlevels > 1:
   3806     return self._getitem_multilevel(key)
-> 3807 indexer = self.columns.get_loc(key)
   3808 if is_integer(indexer):
   3809     indexer = [indexer]

File C:\ProgramData\anaconda3\lib\site-packages\pandas\core\indexes\base.py:3804, in Index.get_loc(self, key, method, tolerance)
   3802     return self._engine.get_loc(casted_key)
   3803 except KeyError as err:
-> 3804     raise KeyError(key) from err
   3805 except TypeError:
   3806     # If we have a listlike key, _check_indexing_error will raise
   3807     #  InvalidIndexError. Otherwise we fall through and re-raise
   3808     #  the TypeError.
   3809     self._check_indexing_error(key)

KeyError: 'Total Emission'

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Generate the data set
num_points = 200
x = np.linspace(0, 1, num_points)
np.random.seed(0)  # for reproducibility
epsilon = np.random.normal(0, 0.2, num_points)
y = 0.3 * np.sin(2 * np.pi * x) + epsilon

# Construct the design matrix
X = np.column_stack([x**3, x**2, x, np.ones_like(x)])

# Train the perceptron (here, linear regression)
model = LinearRegression(fit_intercept=False)
model.fit(X, y)

# Predict using the model
y_pred = model.predict(X)

# Plotting
plt.figure(figsize=(10, 6))
plt.scatter(x, y, s=10, color='blue', label='Generated Data', edgecolors='black')
plt.plot(x, y_pred, color='red', label='Best fit using perceptron')
plt.title('y = 0.3 * np.sin(2 * np.pi * x) + epsilon')
plt.xlabel('x')
plt.ylabel('y(x)')
plt.legend()
plt.grid(True)
plt.show()

# Print the weights
weights = model.coef_
print(f"w3 = {weights[0]:.2f}, w2 = {weights[1]:.2f}, w1 = {weights[2]:.2f}, w0 = {weights[3]:.2f}")

w3 = 5.22, w2 = -7.76, w1 = 2.46, w0 = 0.07

import pandas as pd

# Load the provided Excel file
file_path = 'book_auarterly emissions.csv'
data = pd.read_csv(file_path)

data

from datetime import datetime
import matplotlib.dates as mdates
data_long = pd.melt(data, id_vars=['NC Sector'], var_name='Quarter', value_name='Emissions')


# Correcting the function for converting quarter strings to datetime objects
def quarter_to_datetime(quarter_str):
    parts = quarter_str.split()
    year = int(parts[1])
    quarter = int(parts[0][1])
    month = (quarter - 1) * 3 + 1
    return datetime(year, month, 1)

# Applying the corrected conversion function
data_long['Quarter'] = data_long['Quarter'].apply(quarter_to_datetime)

# Plotting the data
plt.figure(figsize=(15, 8))
for sector in data_long['NC Sector'].unique():
    sector_data = data_long[data_long['NC Sector'] == sector]
    plt.plot(sector_data['Quarter'], sector_data['Emissions'], label=sector)

plt.title('Quarterly Emissions by Sector (2008-2022)')
plt.xlabel('Year')
plt.ylabel('Emissions')
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.legend()
plt.grid(True)
plt.show()

# Calculating total emissions per quarter
total_emissions = data_long.groupby('Quarter').sum()

# Plotting the total emissions
plt.figure(figsize=(15, 8))
plt.plot(total_emissions.index, total_emissions['Emissions'], label='Total Emissions', color='red')

plt.title('Total Quarterly Emissions (2008-2022)')
plt.xlabel('Year')
plt.ylabel('Emissions')
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.grid(True)
plt.legend()
plt.show()

from statsmodels.tsa.seasonal import seasonal_decompose

# Performing a seasonal decomposition of total emissions
decomposition = seasonal_decompose(total_emissions['Emissions'], model='additive', period=4)

# Extracting the trend, seasonal, and residual components
trend = decomposition.trend.dropna()
seasonal = decomposition.seasonal
residual = decomposition.resid.dropna()

# Plotting the decomposition
plt.figure(figsize=(18, 12))

# Trend
plt.subplot(411)
plt.plot(trend, label='Trend', color='blue')
plt.legend(loc='best')
plt.title('Trend in Total Quarterly Emissions')

# Seasonal
plt.subplot(412)
plt.plot(seasonal, label='Seasonality', color='green')
plt.legend(loc='best')
plt.title('Seasonal Variations in Total Quarterly Emissions')

# Residual
plt.subplot(413)
plt.plot(residual, label='Residuals', color='orange')
plt.legend(loc='best')
plt.title('Residuals of Total Quarterly Emissions')

# Original
plt.subplot(414)
plt.plot(total_emissions['Emissions'], label='Original', color='red')
plt.legend(loc='best')
plt.title('Total Quarterly Emissions')

plt.tight_layout()
plt.show()

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Correcting the number of lags to half of the total data points
max_lags = len(total_emissions) // 2 - 1

# Plotting ACF and PACF with the corrected number of lags
fig, axes = plt.subplots(1, 2, figsize=(15, 5))

# Plot the ACF
plot_acf(total_emissions['Emissions'], lags=max_lags, ax=axes[0])

# Plot the PACF
plot_pacf(total_emissions['Emissions'], lags=max_lags, ax=axes[1])

plt.show()

from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.stattools import adfuller
import numpy as np

# Testing for stationarity with the Augmented Dickey-Fuller test
adf_test = adfuller(total_emissions['Emissions'])

# If p-value is greater than 0.05, we conclude that the time series is not stationary
if adf_test[1] > 0.05:
    # Taking the first difference to make the series stationary
    total_emissions['Emissions_diff'] = total_emissions['Emissions'].diff().dropna()
else:
    total_emissions['Emissions_diff'] = total_emissions['Emissions']

# Running the ADF test on the differenced series
adf_test_diff = adfuller(total_emissions['Emissions_diff'].dropna())

# Assuming an ARIMA model with 1 AR term (from PACF) and 1 MA term (from ACF)
# The differencing order is 1 if needed to difference, otherwise, it's 0.
d = 1 if adf_test[1] > 0.05 else 0
p = 1 # from PACF
q = 1 # from ACF

# Fit the ARIMA model
arima_model = ARIMA(total_emissions['Emissions'], order=(p, d, q))
arima_result = arima_model.fit()

# Summary of the model
arima_summary = arima_result.summary()
arima_summary

c:\ProgramData\anaconda_3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.
  self._init_dates(dates, freq)
c:\ProgramData\anaconda_3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.
  self._init_dates(dates, freq)
c:\ProgramData\anaconda_3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.
  self._init_dates(dates, freq)

from sklearn.model_selection import GridSearchCV
import pandas as pd

param_grid = {
    'kernelridge__alpha': [0.1, 1, 10],
    'kernelridge__gamma': np.logspace(-2, 2, 5)
}

# Create a pipeline with polynomial features and kernel ridge regression
kr_model = KernelRidge(kernel='rbf')

# Initialize GridSearchCV with the model and parameter grid
grid_search = GridSearchCV(make_pipeline(kr_model), param_grid, cv=5)

# Fit the grid search to the data
grid_search.fit(X, y)

# Best parameters found
best_params = grid_search.best_params_
best_score = grid_search.best_score_

# Predict using the best model
df['Best Predicted Emission Kernel'] = grid_search.predict(X)

# Plot the original data and the best kernel regression fit
plt.figure(figsize=(14, 7))
plt.scatter(X, y, color='black', label='Actual Emission')
plt.plot(X, df['Best Predicted Emission Kernel'], color='orange', linestyle='--', linewidth=2, label='Optimized Kernel Regression')
plt.title('West Midlands Region Annual Emissions for All Sectors (2005-2021) - Optimized Kernel Regression Fit')
plt.xlabel('Calendar Year')
plt.ylabel('Total Emission')
plt.legend()
plt.show()

(best_params, best_score)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[114], line 22
     19 best_score = grid_search.best_score_
     21 # Predict using the best model
---> 22 df['Best Predicted Emission Kernel'] = grid_search.predict(X)
     24 # Plot the original data and the best kernel regression fit
     25 plt.figure(figsize=(14, 7))

NameError: name 'df' is not defined

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions again
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Plotting the national total emissions time series
plt.figure(figsize=(12, 6))
national_time_series.plot(marker='o', linestyle='-')
plt.title('National Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.grid(True)
plt.show()

# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Plotting the national total emissions time series
plt.figure(figsize=(12, 6))
national_time_series.plot(marker='o', linestyle='-')
plt.title('National Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.grid(True)
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Plotting the national total emissions time series
plt.figure(figsize=(12, 6))
national_time_series.plot(marker='o', linestyle='-')
plt.title('National Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.grid(True)
plt.show()

# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Plotting the national total emissions time series
plt.figure(figsize=(12, 6))
national_time_series.plot(marker='o', linestyle='-')
plt.title('National Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.grid(True)
plt.show()

import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA
import warnings

warnings.filterwarnings("ignore")

# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Function to find the best ARIMA parameters with the lowest AIC value
def best_arima_model(data):
    best_aic = float('inf')
    best_order = None
    for p in range(5):
        for d in range(2):  # Limiting d to 1 as higher orders may not be needed
            for q in range(5):
                try:
                    model = ARIMA(data, order=(p, d, q))
                    model_fit = model.fit()
                    if model_fit.aic < best_aic:
                        best_aic = model_fit.aic
                        best_order = (p, d, q)
                except:
                    continue
    return best_order

# Finding the best ARIMA parameters for 'Grand Total' time series
best_p, best_d, best_q = best_arima_model(national_time_series)

# Print the best parameters
print(f"Best ARIMA Parameters (p, d, q) for 'Grand Total': ({best_p}, {best_d}, {best_q})")

Best ARIMA Parameters (p, d, q) for 'Grand Total': (1, 1, 1)

from statsmodels.tsa.seasonal import seasonal_decompose

# Decompose the time series into trend, seasonal, and residual components
decomposition = seasonal_decompose(national_time_series, model='additive')

# Plotting the decomposition components
plt.figure(figsize=(14, 12))
plt.subplot(4, 1, 1)
plt.plot(national_time_series, label='Original')
plt.legend(loc='upper left')
plt.title('National Total Emissions Over Time')
plt.grid(True)

plt.subplot(4, 1, 2)
plt.plot(decomposition.trend, label='Trend')
plt.legend(loc='upper left')
plt.title('Trend Component')
plt.grid(True)

plt.subplot(4, 1, 3)
plt.plot(decomposition.seasonal, label='Seasonal')
plt.legend(loc='upper left')
plt.title('Seasonal Component')
plt.grid(True)

plt.subplot(4, 1, 4)
plt.plot(decomposition.resid, label='Residual')
plt.legend(loc='upper left')
plt.title('Residual Component')
plt.grid(True)

plt.tight_layout()
plt.show()

# Decompose the time series with a specified period of 1 (yearly data)
decomposition = seasonal_decompose(national_time_series, model='additive', period=1)

# Plotting the decomposition components
plt.figure(figsize=(14, 12))
plt.subplot(4, 1, 1)
plt.plot(national_time_series, label='Original')
plt.legend(loc='upper left')
plt.title('National Total Emissions Over Time')
plt.grid(True)

plt.subplot(4, 1, 2)
plt.plot(decomposition.trend, label='Trend')
plt.legend(loc='upper left')
plt.title('Trend Component')
plt.grid(True)

plt.subplot(4, 1, 3)
plt.plot(decomposition.seasonal, label='Seasonal')
plt.legend(loc='upper left')
plt.title('Seasonal Component')
plt.grid(True)

plt.subplot(4, 1, 4)
plt.plot(decomposition.resid, label='Residual')
plt.legend(loc='upper left')
plt.title('Residual Component')
plt.grid(True)

plt.tight_layout()
plt.show()

from statsmodels.tsa.stattools import adfuller

# Conduct Augmented Dickey-Fuller test
adf_result = adfuller(national_time_series)

# Extract the p-value
p_value = adf_result[1]

p_value

0.9171724950612403

# Apply first-order differencing to the time series
national_time_series_diff = national_time_series.diff().dropna()

# Conduct Augmented Dickey-Fuller test on the differenced series
adf_result_diff = adfuller(national_time_series_diff)

# Extract the p-value for the differenced series
p_value_diff = adf_result_diff[1]

p_value_diff

0.22240697481609656

from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
import warnings

warnings.filterwarnings("ignore")

# Splitting the data into train and test sets
train_size = int(len(time_series_data) * 0.75)
train, test = time_series_data[0:train_size], time_series_data[train_size:]

# Function to find the best ARIMA parameters with the lowest AIC value
def best_arima_model(data):
    best_aic = float('inf')
    best_order = None
    for p in range(5):
        for d in range(2): # limiting d to 1 as our data doesn't seem to require higher order differencing
            for q in range(5):
                try:
                    model = ARIMA(data, order=(p,d,q))
                    model_fit = model.fit()
                    if model_fit.aic < best_aic:
                        best_aic = model_fit.aic
                        best_order = (p,d,q)
                except:
                    continue
    return best_order

# Finding the best ARIMA parameters
best_p, best_d, best_q = best_arima_model(train)

# Using the best parameters to fit the ARIMA model
model = ARIMA(train, order=(best_p, best_d, best_q))
model_fit = model.fit()
forecast = model_fit.forecast(steps=len(test))

best_p, best_d, best_q

# Adjusting the number of lags for the short time series and plotting ACF and PACF again
plt.figure(figsize=(14, 6))

plt.subplot(1, 2, 1)
plot_acf(national_time_series_diff, lags=7, ax=plt.gca())
plt.title('Autocorrelation Function (ACF)')

plt.subplot(1, 2, 2)
plot_pacf(national_time_series_diff, lags=7, ax=plt.gca())
plt.title('Partial Autocorrelation Function (PACF)')

plt.tight_layout()
plt.show()

from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error

# Split the data into train and test sets (using 80% of the data for training)
train_size = int(len(national_time_series) * 0.8)
train, test = national_time_series[0:train_size], national_time_series[train_size:]

# Fit the ARIMA(1,2,2) model
model = ARIMA(train, order=(1,2,2))
model_fit = model.fit(disp=0)

# Forecast emissions for the test set
forecast = model_fit.forecast(steps=len(test))[0]

# Calculate the mean squared error
mse = mean_squared_error(test, forecast)

mse

# Fit the ARIMA(1,2,2) model without the 'disp' argument
model_fit = model.fit()

# Forecast emissions for the test set
forecast = model_fit.forecast(steps=len(test))[0]

# Calculate the mean squared error
mse = mean_squared_error(test, forecast)

mse

# Get the forecasted values as a numpy array
forecast = model_fit.forecast(steps=len(test))

# Calculate the mean squared error
mse = mean_squared_error(test.values, forecast)

mse

# Plotting the actual vs. forecasted emissions
plt.figure(figsize=(12, 6))
plt.plot(train.index, train.values, label='Training Data', color='blue', marker='o')
plt.plot(test.index, test.values, label='Actual Test Data', color='green', marker='o')
plt.plot(test.index, forecast, label='Forecasted Data', color='red', linestyle='--')
plt.title('National Total Emissions: Actual vs. Forecasted')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

from statsmodels.tsa.stattools import adfuller

# Conduct Augmented Dickey-Fuller test
adf_result = adfuller(national_time_series)

# Extract the p-value
p_value = adf_result[1]

p_value

# Apply first-order differencing to the time series
national_time_series_diff = national_time_series.diff().dropna()

# Conduct Augmented Dickey-Fuller test on the differenced series
adf_result_diff = adfuller(national_time_series_diff)

# Extract the p-value for the differenced series
p_value_diff = adf_result_diff[1]

p_value_diff

# Adjusting the number of lags for the short time series and plotting ACF and PACF again
plt.figure(figsize=(14, 6))

plt.subplot(1, 2, 1)
plot_acf(national_time_series_diff, lags=7, ax=plt.gca())
plt.title('Autocorrelation Function (ACF)')

plt.subplot(1, 2, 2)
plot_pacf(national_time_series_diff, lags=7, ax=plt.gca())
plt.title('Partial Autocorrelation Function (PACF)')

plt.tight_layout()
plt.show()

from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error

# Split the data into train and test sets (using 80% of the data for training)
train_size = int(len(national_time_series) * 0.8)
train, test = national_time_series[0:train_size], national_time_series[train_size:]

# Fit the ARIMA(1,2,2) model
model = ARIMA(train, order=(1,2,2))
model_fit = model.fit(disp=0)

# Forecast emissions for the test set
forecast = model_fit.forecast(steps=len(test))[0]

# Calculate the mean squared error
mse = mean_squared_error(test, forecast)

mse

forecast = model_fit.forecast(steps=len(test))


mse = mean_squared_error(test.values, forecast)

mse

plt.figure(figsize=(12, 6))
plt.plot(train.index, train.values, label='Training Data', color='blue', marker='o')
plt.plot(test.index, test.values, label='Actual Test Data', color='green', marker='o')
plt.plot(test.index, forecast, label='Forecasted Data', color='red', linestyle='--')
plt.title('National Total Emissions: Actual vs. Forecasted')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

from sklearn.linear_model import LinearRegression
import numpy as np

# Prepare the data
X = np.array(national_time_series.index).reshape(-1, 1)  # Predictor variable (Year)
y = national_time_series.values  # Response variable (Emissions)

# Initialize the linear regression model
lr_model = LinearRegression()

# Fit the model to the data
lr_model.fit(X, y)

# Predict emissions using the linear regression model
y_pred = lr_model.predict(X)

# Plotting the actual data and the linear regression predictions
plt.figure(figsize=(12, 6))
plt.plot(X, y, label='Actual Emissions', color='blue', marker='o')
plt.plot(X, y_pred, label='Linear Regression Predictions', color='red', linestyle='--')
plt.title('National Total Emissions: Actual vs. Linear Regression Predictions')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Compute the Mean Squared Error (MSE)
mse = mean_squared_error(y, y_pred)

# Compute the Mean Absolute Error (MAE)
mae = mean_absolute_error(y, y_pred)

# Compute the R-squared (R2) score
r2 = r2_score(y, y_pred)

mse, mae, r2

# Introduce a quadratic term
X_quadratic = np.column_stack((X, X**2))

# Initialize and fit the quadratic regression model
lr_quadratic = LinearRegression()
lr_quadratic.fit(X_quadratic, y)

# Predict emissions using the quadratic regression model
y_pred_quadratic = lr_quadratic.predict(X_quadratic)

# Compute performance metrics for the quadratic model
mse_quadratic = mean_squared_error(y, y_pred_quadratic)
mae_quadratic = mean_absolute_error(y, y_pred_quadratic)
r2_quadratic = r2_score(y, y_pred_quadratic)

mse_quadratic, mae_quadratic, r2_quadratic

# Plotting the actual data, linear regression predictions, and quadratic regression predictions
plt.figure(figsize=(12, 6))
plt.plot(X, y, label='Actual Emissions', color='blue', marker='o')
plt.plot(X, y_pred, label='Linear Regression Predictions', color='green', linestyle='--')
plt.plot(X, y_pred_quadratic, label='Quadratic Regression Predictions', color='red', linestyle='--')
plt.title('National Total Emissions: Actual vs. Regression Predictions')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

from sklearn.preprocessing import PolynomialFeatures

# Generate polynomial features (up to degree 3)
poly = PolynomialFeatures(degree=3)
X_poly = poly.fit_transform(X)

# Initialize and fit the polynomial regression model
lr_poly = LinearRegression()
lr_poly.fit(X_poly, y)

# Predict emissions using the polynomial regression model
y_pred_poly = lr_poly.predict(X_poly)

# Plotting the actual data and the polynomial regression predictions
plt.figure(figsize=(12, 6))
plt.plot(X, y, label='Actual Emissions', color='blue', marker='o')
plt.plot(X, y_pred_poly, label='Polynomial Regression Predictions (Degree 3)', color='purple', linestyle='--')
plt.title('National Total Emissions: Actual vs. Polynomial Regression Predictions')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV

# Define the Ridge regression model
ridge = Ridge()

# Define hyperparameter space
param_grid = {
    'alpha': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000]  # Regularization strength values
}

# Initialize grid search
grid_search = GridSearchCV(ridge, param_grid, scoring='neg_mean_squared_error', cv=5)

# Perform grid search on the polynomial features
grid_search.fit(X_poly, y)

# Extract best hyperparameters and the corresponding score
best_params = grid_search.best_params_
best_score = grid_search.best_score_

best_params, best_score

# Using the best Ridge regression model to make predictions
best_ridge = grid_search.best_estimator_
y_pred_ridge = best_ridge.predict(X_poly)

# Plotting the actual data, polynomial regression predictions, and Ridge regression predictions
plt.figure(figsize=(12, 6))
plt.plot(X, y, label='Actual Emissions', color='blue', marker='o')
plt.plot(X, y_pred_poly, label='Polynomial Regression Predictions (Degree 3)', color='purple', linestyle='--')
plt.plot(X, y_pred_ridge, label='Ridge Regression Predictions (Degree 3, Alpha=1000)', color='orange', linestyle='--')
plt.title('National Total Emissions: Actual vs. Regression Predictions')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV

# Define the Ridge regression model
ridge = Ridge()

# Define hyperparameter space
param_grid = {
    'alpha': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000]  # Regularization strength values
}

# Initialize grid search
grid_search = GridSearchCV(ridge, param_grid, scoring='neg_mean_squared_error', cv=5)

# Perform grid search on the polynomial features
grid_search.fit(X_poly, y)

# Extract best hyperparameters and the corresponding score
best_params = grid_search.best_params_
best_score = grid_search.best_score_

best_params, best_score

# Using the best Ridge regression model to make predictions
best_ridge = grid_search.best_estimator_
y_pred_ridge = best_ridge.predict(X_poly)

# Plotting the actual data, polynomial regression predictions, and Ridge regression predictions
plt.figure(figsize=(12, 6))
plt.plot(X, y, label='Actual Emissions', color='blue', marker='o')
plt.plot(X, y_pred_poly, label='Polynomial Regression Predictions (Degree 3)', color='purple', linestyle='--')
plt.plot(X, y_pred_ridge, label='Ridge Regression Predictions (Degree 3, Alpha=1000)', color='orange', linestyle='--')
plt.title('National Total Emissions: Actual vs. Regression Predictions')
plt.xlabel('Year')
plt.ylabel('Total Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# Reshape the data
X = np.array(national_time_series.index).reshape(-1, 1)  # Predictor variable (Year)

# Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)

# Scale the data (important for neural network convergence)
scaler_X = StandardScaler().fit(X_train)
scaler_y = StandardScaler().fit(y_train.reshape(-1, 1))

X_train_scaled = scaler_X.transform(X_train)
X_test_scaled = scaler_X.transform(X_test)
y_train_scaled = scaler_y.transform(y_train.reshape(-1, 1))
y_test_scaled = scaler_y.transform(y_test.reshape(-1, 1))

X_train_scaled.shape, X_test_scaled.shape, y_train_scaled.shape, y_test_scaled.shape

# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Prepare the data again
X = np.array(national_time_series.index).reshape(-1, 1)  # Predictor variable (Year)

# Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)

# Scale the data
scaler_X = StandardScaler().fit(X_train)
scaler_y = StandardScaler().fit(y_train.reshape(-1, 1))

X_train_scaled = scaler_X.transform(X_train)
X_test_scaled = scaler_X.transform(X_test)
y_train_scaled = scaler_y.transform(y_train.reshape(-1, 1))
y_test_scaled = scaler_y.transform(y_test.reshape(-1, 1))

X_train_scaled.shape, X_test_scaled.shape, y_train_scaled.shape, y_test_scaled.shape

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Convert the 'Grand Total' column to a numeric type
data_1_1_actual['Grand Total'] = pd.to_numeric(data_1_1_actual['Grand Total'], errors='coerce')

# Filter the data for 'National Total' and group by 'Calendar Year' to get yearly national emissions
national_emissions = data_1_1_actual[data_1_1_actual['Region/Country'] == 'National Total']
national_time_series = national_emissions.groupby('Calendar Year')['Grand Total'].sum()

# Prepare the data again
X = np.array(national_time_series.index).reshape(-1, 1)  # Predictor variable (Year)

# Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)

# Scale the data
scaler_X = StandardScaler().fit(X_train)
scaler_y = StandardScaler().fit(y_train.reshape(-1, 1))

X_train_scaled = scaler_X.transform(X_train)
X_test_scaled = scaler_X.transform(X_test)
y_train_scaled = scaler_y.transform(y_train.reshape(-1, 1))
y_test_scaled = scaler_y.transform(y_test.reshape(-1, 1))

X_train_scaled.shape, X_test_scaled.shape, y_train_scaled.shape, y_test_scaled.shape

# Define the target variable (Emissions)
y = national_time_series.values

# Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)

# Scale the data
scaler_X = StandardScaler().fit(X_train)
scaler_y = StandardScaler().fit(y_train.reshape(-1, 1))

X_train_scaled = scaler_X.transform(X_train)
X_test_scaled = scaler_X.transform(X_test)
y_train_scaled = scaler_y.transform(y_train.reshape(-1, 1))
y_test_scaled = scaler_y.transform(y_test.reshape(-1, 1))

X_train_scaled.shape, X_test_scaled.shape, y_train_scaled.shape, y_test_scaled.shape

# TensorFlow and Keras imports
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout

# Define the neural network model with dropout
model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),
    Dropout(0.5),
    Dense(32, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='linear')
])

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
history = model.fit(X_train_scaled, y_train_scaled, epochs=500, validation_data=(X_test_scaled, y_test_scaled), verbose=0)

# Plotting the training and validation loss over epochs
plt.figure(figsize=(10, 5))
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Mean Squared Error')
plt.legend()
plt.grid(True)
plt.show()

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout

model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),
    Dropout(0.5),
    Dense(32, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='linear')
])

model.compile(optimizer='adam', loss='mean_squared_error')

history = model.fit(X_train_scaled, y_train_scaled, epochs=500, validation_data=(X_test_scaled, y_test_scaled))

import matplotlib.pyplot as plt

# Predict emissions for training and validation sets
y_train_pred_scaled = model.predict(X_train_scaled)
y_test_pred_scaled = model.predict(X_test_scaled)

# Transform predictions back to original domain
y_train_pred = scaler_y.inverse_transform(y_train_pred_scaled)
y_test_pred = scaler_y.inverse_transform(y_test_pred_scaled)

# Plot actual vs. predicted emissions for the training set
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.scatter(X_train, y_train, color='blue', label='Actual')
plt.scatter(X_train, y_train_pred, color='red', label='Predicted', marker='x')
plt.title('Training Set: Actual vs. Predicted Emissions')
plt.xlabel('Year')
plt.ylabel('Emissions')
plt.legend()

# Plot actual vs. predicted emissions for the validation set
plt.subplot(1, 2, 2)
plt.scatter(X_test, y_test, color='blue', label='Actual')
plt.scatter(X_test, y_test_pred, color='red', label='Predicted', marker='x')
plt.title('Validation Set: Actual vs. Predicted Emissions')
plt.xlabel('Year')
plt.ylabel('Emissions')
plt.legend()

plt.tight_layout()
plt.show()

from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.metrics import r2_score

# Compute the Mean Absolute Error (MAE)
mae = mean_absolute_error(y_test, y_test_pred)

# Compute the Mean Squared Error (MSE)
mse = mean_squared_error(y_test, y_test_pred)

# Compute the R^2 score
r2 = r2_score(y_test, y_test_pred)

print(f"Mean Absolute Error (MAE) on Test Set: {mae:.2f}")
print(f"Mean Squared Error (MSE) on Test Set: {mse:.2f}")
print(f"R^2 Score on Test Set: {r2:.2f}")

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.initializers import HeNormal

# Initialize the model
model = Sequential()

# Input layer
model.add(Dense(64, activation='relu', kernel_initializer=HeNormal(), input_shape=(X_train_scaled.shape[1],)))
model.add(Dropout(0.3))

# Hidden layers
model.add(Dense(32, activation='relu', kernel_initializer=HeNormal()))
model.add(Dropout(0.3))

model.add(Dense(16, activation='relu', kernel_initializer=HeNormal()))
model.add(Dropout(0.3))

# Output layer
model.add(Dense(1, activation='linear'))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model (assuming X_train_scaled, y_train_scaled are your training data)
history = model.fit(X_train_scaled, y_train_scaled, epochs=500, batch_size=32, validation_data=(X_test_scaled, y_test_scaled))

import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

# Predict emissions for the validation set
y_val_pred = model.predict(X_val_scaled)

# Visual Inspection
plt.figure(figsize=(10, 5))
plt.scatter(X_val, y_val, color='blue', label='Actual')
plt.scatter(X_val, y_val_pred, color='red', s=10, label='Predicted')
plt.title('Validation Set: Actual vs. Predicted Emissions')
plt.xlabel('Year')
plt.ylabel('Emissions')
plt.legend()
plt.grid(True)
plt.show()

# Quantitative Evaluation
mae = mean_absolute_error(y_val, y_val_pred)
mse = mean_squared_error(y_val, y_val_pred)
r2 = r2_score(y_val, y_val_pred)

print(f"Mean Absolute Error (MAE) on Validation Set: {mae:.2f}")
print(f"Mean Squared Error (MSE) on Validation Set: {mse:.2f}")
print(f"R^2 Score on Validation Set: {r2:.2f}")

import matplotlib.pyplot as plt

# Assuming df is your dataframe and 'feature_name' is the name of a feature
plt.boxplot(data_1_1_actual['Grand Total'])
plt.title('Boxplot of feature_name')
plt.show()

Q1 = data_1_1_actual['Grand Total'].quantile(0.25)
Q3 = data_1_1_actual['Grand Total'].quantile(0.75)
IQR = Q3 - Q1
outliers = data_1_1_actual[(data_1_1_actual['Grand Total'] < (Q1 - 1.5 * IQR)) | (data_1_1_actual['Grand Total'] > (Q3 + 1.5 * IQR))]

# Loading the data from sheet "5_1"
data_5_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name="5_1")

# Displaying the first few rows of the data
data_5_1.head()

# Skipping the initial rows to get to the actual data
data_5_1_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", 
                                 sheet_name="5_1", skiprows=4)

# Displaying the first few rows of the cleaned data
data_5_1_cleaned.head()

# Dropping the first row (header info)
data_5_1_cleaned = data_5_1_cleaned.drop(0)

# Calculating the total emissions for each year
total_emissions_per_year = data_5_1_cleaned.iloc[:, 6:].sum()

total_emissions_per_year

# Visualizing the total emissions over the years
plt.figure(figsize=(14, 7))
total_emissions_per_year.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# Grouping by regions and summing up the emissions for the entire period (2005-2021)
total_emissions_by_region = data_5_1_cleaned.groupby('Area')[data_5_1_cleaned.columns[6:]].sum().sum(axis=1)

# Identifying regions with the highest and lowest emissions
highest_emission_region = total_emissions_by_region.idxmax()
lowest_emission_region = total_emissions_by_region.idxmin()

highest_emission_value = total_emissions_by_region.max()
lowest_emission_value = total_emissions_by_region.min()

highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value

# Displaying the column names to identify the correct column for regions
data_5_1_cleaned.columns

# Displaying unique values in the first few columns to identify the regions
unique_values_in_columns = {}

for col in data_5_1_cleaned.columns[:5]:
    unique_values_in_columns[col] = data_5_1_cleaned[col].unique()

unique_values_in_columns

# Grouping by regions and summing up the emissions for the entire period (2005-2021)
total_emissions_by_region = data_5_1_cleaned.groupby("Freeze panes are active on this sheet. To turn off freeze panes select the 'View' ribbon then 'Freeze Panes' then 'Unfreeze Panes' or use [Alt W, F].")[data_5_1_cleaned.columns[6:]].sum().sum(axis=1)

# Identifying regions with the highest and lowest emissions
highest_emission_region = total_emissions_by_region.idxmax()
lowest_emission_region = total_emissions_by_region.idxmin()

highest_emission_value = total_emissions_by_region.max()
lowest_emission_value = total_emissions_by_region.min()

highest_emission_region, highest_emission_value, lowest_emission_region, lowest_emission_value

# Grouping by companies/entities and summing up the emissions for the entire period (2005-2021)
total_emissions_by_entity = data_5_1_cleaned.groupby("Unnamed: 1")[data_5_1_cleaned.columns[6:]].sum().sum(axis=1)

# Identifying entities/companies with the highest emissions
top_5_entities = total_emissions_by_entity.nlargest(5)

top_5_entities

# Preparing the data for time series analysis
# We'll consider the total emissions for each year as our time series data
time_series_data = total_emissions_per_year

# Plotting the data for visual inspection
plt.figure(figsize=(14, 7))
time_series_data.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
import warnings

warnings.filterwarnings("ignore")

# Splitting the data into train and test sets
train_size = int(len(time_series_data) * 0.75)
train, test = time_series_data[0:train_size], time_series_data[train_size:]

# Function to find the best ARIMA parameters with the lowest AIC value
def best_arima_model(data):
    best_aic = float('inf')
    best_order = None
    for p in range(5):
        for d in range(2): # limiting d to 1 as our data doesn't seem to require higher order differencing
            for q in range(5):
                try:
                    model = ARIMA(data, order=(p,d,q))
                    model_fit = model.fit()
                    if model_fit.aic < best_aic:
                        best_aic = model_fit.aic
                        best_order = (p,d,q)
                except:
                    continue
    return best_order

# Finding the best ARIMA parameters
best_p, best_d, best_q = best_arima_model(train)

# Using the best parameters to fit the ARIMA model
model = ARIMA(train, order=(best_p, best_d, best_q))
model_fit = model.fit()
forecast = model_fit.forecast(steps=len(test))

best_p, best_d, best_q

# Visualizing the actual vs. forecasted emissions
plt.figure(figsize=(14, 7))
plt.plot(train.index, train, label='Train', marker='o')
plt.plot(test.index, test, label='Test', marker='o')
plt.plot(test.index, forecast, label='Forecast', marker='o', linestyle='--')
plt.title('Actual vs. Forecasted Emissions')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

data_5_1

# Grouping data by 'Post Code' and summing up the total emissions
postcode_grouped_data = data_5_1.groupby('Post Code').sum()

# Identifying the total emission for 'West Midlands' 
west_midlands_emission = postcode_grouped_data.loc['West Midlands', '2005':]
west_midlands_total_emission = west_midlands_emission.sum()

west_midlands_total_emission

# Loading the data from the "5_1" sheet
data_5_1_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", 
                                 sheet_name="5_1", skiprows=4)

# Dropping the first row (header info)
data_5_1_cleaned = data_5_1_cleaned.drop(0)

# Calculating the total emissions for each year
total_emissions_per_year = data_5_1_cleaned.iloc[:, 6:].sum()

# Renaming the index to match years
years = list(range(2005, 2022))  # Years from 2005 to 2021
total_emissions_per_year.index = years

# Visualizing the total emissions over the years
plt.figure(figsize=(14, 7))
total_emissions_per_year.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time WestMidlands (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(years, rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
import warnings

warnings.filterwarnings("ignore")

# Splitting the data into train and test sets
train_size = int(len(time_series_data) * 0.75)
train, test = time_series_data[0:train_size], time_series_data[train_size:]

# Function to find the best ARIMA parameters with the lowest AIC value
def best_arima_model(data):
    best_aic = float('inf')
    best_order = None
    for p in range(5):
        for d in range(2): # limiting d to 1 as our data doesn't seem to require higher order differencing
            for q in range(5):
                try:
                    model = ARIMA(data, order=(p,d,q))
                    model_fit = model.fit()
                    if model_fit.aic < best_aic:
                        best_aic = model_fit.aic
                        best_order = (p,d,q)
                except:
                    continue
    return best_order

# Finding the best ARIMA parameters
best_p, best_d, best_q = best_arima_model(train)

# Using the best parameters to fit the ARIMA model
model = ARIMA(train, order=(best_p, best_d, best_q))
model_fit = model.fit()
forecast = model_fit.forecast(steps=len(test))

best_p, best_d, best_q

# Calculating the total emissions for each year
total_emissions_per_year = data_5_1_cleaned.iloc[:, 6:].sum()

# Renaming the index to match years
years = list(range(2005, 2022))  # Years from 2005 to 2021
total_emissions_per_year.index = years


# Visualizing the actual vs. forecasted emissions
plt.figure(figsize=(14, 7))
plt.plot(train.index, train, label='Train', marker='o')
plt.plot(test.index, test, label='Test', marker='o')
plt.plot(test.index, forecast, label='Forecast', marker='o', linestyle='--')
plt.title('Actual vs. Forecasted Emissions')
plt.xlabel('Years')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

# Plotting the total emissions over the years again with proper x-axis labels
plt.figure(figsize=(14, 7))
total_emissions_per_year.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(years, rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# Plotting the total emissions over the years again with proper x-axis labels
plt.figure(figsize=(14, 7))
total_emissions_per_year.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(ticks=years, labels=years, rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# Setting up the libraries and data again
import pandas as pd
import matplotlib.pyplot as plt

# Loading the data from the "5_1" sheet again
data_5_1_cleaned = pd.read_excel("/mnt/data/2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", 
                                 sheet_name="5_1", skiprows=4)
data_5_1_cleaned = data_5_1_cleaned.drop(0)
total_emissions_per_year = data_5_1_cleaned.iloc[:, 6:].sum()
years = list(range(2005, 2022))

# Plotting the total emissions over the years with proper x-axis labels
plt.figure(figsize=(14, 7))
total_emissions_per_year.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(ticks=years, labels=years, rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# Re-running the optimized code

# 1. Data Preparation
data_5_1_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", 
                                 sheet_name="5_1", skiprows=4)
data_5_1_cleaned = data_5_1_cleaned.drop(0)
total_emissions_per_year = data_5_1_cleaned.iloc[:, 6:].sum()
years = list(range(2005, 2022))
total_emissions_per_year.index = years

# 2. Plotting Emissions Over Time
plt.figure(figsize=(14, 7))
total_emissions_per_year.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time UK (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(years, rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# 3. Time Series Forecasting with ARIMA
train_size = int(len(total_emissions_per_year) * 0.75)
train, test = total_emissions_per_year[0:train_size], total_emissions_per_year[train_size:]

best_p, best_d, best_q = best_arima_model(train)
model = ARIMA(train, order=(best_p, best_d, best_q))
model_fit = model.fit()
forecast = model_fit.forecast(steps=len(test))

# 4. Plotting Actual vs. Forecasted Emissions
plt.figure(figsize=(14, 7))
plt.plot(train.index, train, label='Train', marker='o')
plt.plot(test.index, test, label='Test', marker='o')
plt.plot(test.index, forecast, label='Forecast', marker='o', linestyle='--')
plt.title('Actual vs. Forecasted Emissions')
plt.xlabel('Years')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(years, rotation=45)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

best_p, best_d, best_q

# 1. Data Preparation
url = 'https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1166194/2005-21-uk-local-authority-ghg-emissions.xlsx'
data_5_1_cleaned = pd.read_excel(url, sheet_name='5_1', skiprows=4)

data_5_1_cleaned = data_5_1_cleaned.drop(0)
total_emissions_per_year = data_5_1_cleaned.iloc[:, 6:].sum()
years = list(range(2005, 2022))
total_emissions_per_year.index = years

# 2. Plotting Emissions Over Time
plt.figure(figsize=(14, 7))
total_emissions_per_year.plot(marker='o', linestyle='-')
plt.title('Total Emissions Over Time in Uk (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(years, rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# 3. Time Series Forecasting with ARIMA
train_size = int(len(total_emissions_per_year) * 0.75)
train, test = total_emissions_per_year[0:train_size], total_emissions_per_year[train_size:]

best_p, best_d, best_q = best_arima_model(train)
model = ARIMA(train, order=(best_p, best_d, best_q))
model_fit = model.fit()
forecast = model_fit.forecast(steps=len(test))

# 4. Plotting Actual vs. Forecasted Emissions
plt.figure(figsize=(14, 7))
plt.plot(train.index, train, label='Train',  color='blue', marker='o')
plt.plot(test.index, test, label='Test', color='green', marker='o')
plt.plot(test.index, forecast, label='Forecast',color='red',  marker='o', linestyle='--')


plt.title('Actual vs. Forecasted Emissions')
plt.xlabel('Years')
plt.ylabel('Emissions (kt CO2e)')
plt.xticks(years, rotation=45)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

best_p, best_d, best_q

# Loading the cleaned dataset from sheet "5_1"
data_5_1_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name="5_1", skiprows=4)

# List of local authority district names in West Midlands
west_midlands_districts = [
    "Lichfield", "Newport", "Rugby", "Shropshire", "Staffordshire Moorlands",
    "Birmingham", "Stoke-on-Trent", "South Staffordshire", "Coventry", "Cannock Chase",
    "Dudley", "Stafford", "Sutton", "Telford and Wrekin", "Wolverhampton", 
    "Tamworth", "Walsall", "Warwick", "Sandwell", "Herefordshire, County of",
    "East Staffordshire", "Stratford-on-Avon", "Newcastle-under-Lyme", "Bromsgrove", 
    "Solihull", "Nuneaton and Bedworth", "Redditch", "Wyre Forest", "Worcester"
]

# Filtering the dataset for only the West Midlands districts
west_midlands_data = data_5_1_cleaned[data_5_1_cleaned['Local Authority District name (2013 boundaries)'].isin(west_midlands_districts)]

# Displaying the first few rows of the West Midlands data
west_midlands_data.head()

import pandas as pd

# Loading the cleaned dataset from sheet "5_1"
data_5_1_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name="5_1", skiprows=4)

# Filtering the dataset for only the West Midlands districts
west_midlands_data = data_5_1_cleaned[data_5_1_cleaned['Local Authority District name (2013 boundaries)'].isin(west_midlands_districts)]

# Displaying the first few rows of the West Midlands data
west_midlands_data.head()

# Displaying the columns of the cleaned data to identify the correct name for "Local Authority District name (2013 boundaries)"
data_5_1_cleaned.columns

# Displaying the first few rows of the cleaned dataset to understand its structure
data_5_1_cleaned.head()

# Adjusting the dataset to use the correct column names and dropping unnecessary rows
data_5_1_cleaned.columns = data_5_1_cleaned.iloc[0]
data_5_1_cleaned = data_5_1_cleaned.drop(0)

# Filtering the dataset for only the West Midlands districts
west_midlands_data = data_5_1_cleaned[data_5_1_cleaned['Local Authority District Name'].isin(west_midlands_districts)]

# Displaying the first few rows of the West Midlands data
west_midlands_data.head()

# List of local authority district names in West Midlands
west_midlands_districts = [
    "Lichfield", "Newport", "Rugby", "Shropshire", "Staffordshire Moorlands",
    "Birmingham", "Stoke-on-Trent", "South Staffordshire", "Coventry", "Cannock Chase",
    "Dudley", "Stafford", "Sutton", "Telford and Wrekin", "Wolverhampton", 
    "Tamworth", "Walsall", "Warwick", "Sandwell", "Herefordshire, County of",
    "East Staffordshire", "Stratford-on-Avon", "Newcastle-under-Lyme", "Bromsgrove", 
    "Solihull", "Nuneaton and Bedworth", "Redditch", "Wyre Forest", "Worcester"
]

# Filtering the dataset for only the West Midlands districts
west_midlands_data = data_5_1_cleaned[data_5_1_cleaned['Local Authority District Name'].isin(west_midlands_districts)]

# Displaying the first few rows of the West Midlands data
west_midlands_data.head()

# Summarizing the total emissions over the years for the West Midlands districts
yearly_emissions_west_midlands = west_midlands_data.iloc[:, 5:].sum().reset_index()
yearly_emissions_west_midlands.columns = ['Year', 'Total Emissions']

# Displaying the summarized yearly emissions for West Midlands
yearly_emissions_west_midlands

# Summarizing the total emissions over the years for the West Midlands districts without resetting the index
yearly_emissions_west_midlands = west_midlands_data.iloc[:, 5:].sum()

# Converting the series to a dataframe for better visualization
yearly_emissions_west_midlands_df = yearly_emissions_west_midlands.reset_index()
yearly_emissions_west_midlands_df.columns = ['Year', 'Total Emissions']

# Displaying the summarized yearly emissions for West Midlands
yearly_emissions_west_midlands_df

# Summarizing the total emissions over the years for the West Midlands districts without the additional index column
yearly_emissions_west_midlands = west_midlands_data.groupby('Local Authority District Name').sum().sum(axis=0)
yearly_emissions_west_midlands = yearly_emissions_west_midlands.drop('Row Labels').reset_index()
yearly_emissions_west_midlands.columns = ['Year', 'Total Emissions']

# Displaying the summarized yearly emissions for West Midlands
yearly_emissions_west_midlands

# Displaying the summarized yearly emissions for West Midlands without attempting to drop the 'Row Labels'
yearly_emissions_west_midlands

import matplotlib.pyplot as plt

# Plotting the emissions over time for visual inspection of anomalies
plt.figure(figsize=(14, 7))
plt.plot(yearly_emissions_west_midlands.index, yearly_emissions_west_midlands.values, marker='o', linestyle='-')
plt.title("Yearly Emissions for All Emissions West Midlands Districts (2005-2021)")
plt.xlabel("Year")
plt.ylabel("Total Emissions (ktCO₂e)")
plt.grid(True)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# Extracting unique sector names from the dataset
sectors = west_midlands_data['Sector Name'].unique()

sectors

# Extracting unique sector names from the dataset
sectors = west_midlands_data['Sector Name'].unique()

sectors

# Checking the column names in the dataset
west_midlands_data.columns

# Grouping by 'Operator' and summing the emissions for each year
operator_emissions = west_midlands_data.groupby('Operator').sum()

# Identifying the top 10 emitting operators over the entire period (2005-2021)
top_operators = operator_emissions.sum(axis=1).nlargest(10)

top_operators

import matplotlib.pyplot as plt

# Extracting emission data for the top operators
top_operator_data = operator_emissions.loc[top_operators.index]

# Plotting the emissions trends of top operators over time
plt.figure(figsize=(15, 8))
for operator in top_operator_data.index:
    plt.plot(top_operator_data.columns, top_operator_data.loc[operator], label=operator, marker='o')
    
plt.title('Emission Trends of Top Operators (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (ktCO₂e)')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True, which='both', linestyle='--', linewidth=0.5)
plt.tight_layout()
plt.show()

from scipy.stats import linregress

# Calculate the linear regression slope for each operator's emissions over the years
slopes = {}
for operator, row in operator_emissions.iterrows():
    slope, _, _, _, _ = linregress(range(len(row)), row.values)
    slopes[operator] = slope

# Filter operators with positive slopes (indicating an upward trend)
increasing_operators = {operator: slope for operator, slope in slopes.items() if slope > 0}

# Rank operators based on the slope value
sorted_increasing_operators = dict(sorted(increasing_operators.items(), key=lambda item: item[1], reverse=True))

sorted_increasing_operators

# Loading the cleaned dataset from sheet "2_1"
data_2_1_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name="2_1", skiprows=4)

# List of local authority district names in West Midlands
west_midlands_districts = [
    "Lichfield", "Newport", "Rugby", "Shropshire", "Staffordshire Moorlands",
    "Birmingham", "Stoke-on-Trent", "South Staffordshire", "Coventry", "Cannock Chase",
    "Dudley", "Stafford", "Sutton", "Telford and Wrekin", "Wolverhampton", 
    "Tamworth", "Walsall", "Warwick", "Sandwell", "Herefordshire, County of",
    "East Staffordshire", "Stratford-on-Avon", "Newcastle-under-Lyme", "Bromsgrove", 
    "Solihull", "Nuneaton and Bedworth", "Redditch", "Wyre Forest", "Worcester"
]

# Displaying the column names of the dataset from sheet "2_1" to identify the correct name for the local authority district
data_2_1_cleaned.columns

# Filtering the dataset for only the West Midlands districts using the correct column name
west_midlands_data_2_1 = data_2_1_cleaned[data_2_1_cleaned['Local Authority'].isin(west_midlands_districts)]

# Grouping by year and summing up the emissions for all West Midlands districts
west_midlands_yearly_emissions = west_midlands_data_2_1.groupby('Calendar Year')['Grand Total'].sum().reset_index()

# Displaying the yearly emissions for the West Midlands districts
west_midlands_yearly_emissions

# Columns of interest for emissions analysis
emission_sections = [
    "Industry Electricity", "Industry Gas", "Large Industrial Installations", "Industry 'Other'", "Industry Total",
    "Commercial Electricity", "Commercial Gas", "Commercial 'Other'", "Commercial Total",
    "Public Sector Electricity", "Public Sector Gas", "Public Sector 'Other'", "Public Sector Total",
    "Domestic Electricity", "Domestic Gas", "Domestic 'Other'", "Domestic Total",
    "Road Transport (A roads)", "Road Transport (Minor roads)", "Transport 'Other'", "Transport Total",
    "Agriculture Electricity", "Agriculture Gas", "Agriculture 'Other'", "Agriculture Total",
    "Waste Management 'Other'", "Waste Management Total"
]

# Summing up the emissions for each section across all years
total_emissions_by_section = west_midlands_data_2_1[emission_sections].sum().sort_values(ascending=False)

# Extracting the top 5 emission sections
top_5_emission_sections = total_emissions_by_section.head(5)
top_5_emission_sections

# Checking the available columns in the dataset
available_columns = west_midlands_data_2_1.columns

# Filtering out the columns from the emission_sections list that are not in the available columns
valid_emission_sections = [col for col in emission_sections if col in available_columns]

valid_emission_sections

# Summing up the emissions for each valid section across all years
total_emissions_by_valid_section = west_midlands_data_2_1[valid_emission_sections].sum().sort_values(ascending=False)

# Extracting the top 5 emission sections from the valid sections
top_5_emission_valid_sections = total_emissions_by_valid_section.head(5)
top_5_emission_valid_sections

# Grouping by "Calendar Year" and summing the emissions for top 5 sections
yearly_emissions = west_midlands_data_2_1.groupby("Calendar Year")[top_5_emission_valid_sections.index].sum()

# Plotting the trends
plt.figure(figsize=(15, 10))
for section in top_5_emission_valid_sections.index:
    plt.plot(yearly_emissions.index, yearly_emissions[section], label=section, marker='o')

plt.title('Emission Trends for Top 5 Emission Sources in West Midlands (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.legend()
plt.grid(True)
plt.show()

# Loading the cleaned dataset from sheet "2_1"
data_1_2_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name="1_2", skiprows=4)

# List of local authority district names in West Midlands
west_midlands_districts = [
    "Lichfield", "Newport", "Rugby", "Shropshire", "Staffordshire Moorlands",
    "Birmingham", "Stoke-on-Trent", "South Staffordshire", "Coventry", "Cannock Chase",
    "Dudley", "Stafford", "Sutton", "Telford and Wrekin", "Wolverhampton", 
    "Tamworth", "Walsall", "Warwick", "Sandwell", "Herefordshire, County of",
    "East Staffordshire", "Stratford-on-Avon", "Newcastle-under-Lyme", "Bromsgrove", 
    "Solihull", "Nuneaton and Bedworth", "Redditch", "Wyre Forest", "Worcester"
]

# Displaying the column names of the dataset from sheet "1_2" to identify the correct name for the local authority district
data_1_2_cleaned.columns

# Filtering the dataset for only the West Midlands districts using the correct column name
west_midlands_data_1_2 = data_1_2_cleaned[data_1_2_cleaned['Local Authority'].isin(west_midlands_districts)]

# Grouping by year and summing up the emissions for all West Midlands districts
west_midlands_yearly_emissions = west_midlands_data_1_2.groupby('Calendar Year')['Grand Total'].sum().reset_index()

# Displaying the yearly emissions for the West Midlands districts
west_midlands_yearly_emissions

# Displaying the columns of the dataset to identify the correct column names
data_1_2_cleaned.columns

# Correcting the column names with extra spaces
columns_to_consider_corrected = [
    col if not col.endswith('Gas') else col + ' ' for col in columns_to_consider
]

# Sum the emissions across all years for each corrected column
emissions_sum_corrected = west_midlands_data_1_2[columns_to_consider_corrected].sum()

# Sort the columns based on the total emissions and select top 5
top_5_emissions_sources_corrected = emissions_sum_corrected.sort_values(ascending=False).head(5)
top_5_emissions_sources_corrected

# Correcting the column names based on the dataset's actual columns
corrected_columns = [col if col not in ['Domestic Gas', 'Agriculture Gas'] else col for col in columns_to_consider_corrected]

# Sum the emissions across all years for each corrected column
emissions_sum_final = west_midlands_data_1_2[corrected_columns].sum()

# Sort the columns based on the total emissions and select top 5
top_5_emissions_sources_final = emissions_sum_final.sort_values(ascending=False).head(5)
top_5_emissions_sources_final

# Directly using the known columns for emission sources
emission_columns = [
    "Industry Electricity", "Industry Gas ", "Large Industrial Installations", "Industry 'Other'", "Industry Total",
    "Commercial Electricity", "Commercial Gas ", "Commercial 'Other'", "Commercial Total",
    "Public Sector Electricity", "Public Sector Gas ", "Public Sector 'Other'", "Public Sector Total",
    "Domestic Electricity", "Domestic Gas ", "Domestic 'Other'", "Domestic Total",
    "Road Transport (A roads)", "Road Transport (Motorways)", "Road Transport (Minor roads)", "Diesel Railways", 
    "Transport 'Other'", "Transport Total", "Net Emissions: Forest land", "Net Emissions: Cropland",
    "Net Emissions: Grassland", "Net Emissions: Wetlands", "Net Emissions: Settlements", 
    "Net Emissions: Harvested Wood Products", "Net Emissions: Indirect N2O", "LULUCF Net Emissions",
    "Agriculture Electricity", "Agriculture Gas ", "Agriculture 'Other'", "Agriculture Livestock", 
    "Agriculture Soils", "Agriculture Total", "Landfill", "Waste Management 'Other'", "Waste Management Total"
]

# Sum the emissions across all years for each emission column
emissions_sum_actual = west_midlands_data_1_2[emission_columns].sum()

# Sort the columns based on the total emissions and select top 5
top_5_emissions_sources_actual = emissions_sum_actual.sort_values(ascending=False).head(5)
top_5_emissions_sources_actual

# Identify the missing columns from the dataset
missing_columns = [col for col in emission_columns if col not in west_midlands_data_1_2.columns]
missing_columns

# Excluding the missing columns
emission_columns_excluding_missing = [col for col in emission_columns if col not in missing_columns]

# Sum the emissions across all years for each emission column excluding the missing ones
emissions_sum_excluding_missing = west_midlands_data_1_2[emission_columns_excluding_missing].sum()

# Sort the columns based on the total emissions and select top 5
top_5_emissions_sources_excluding_missing = emissions_sum_excluding_missing.sort_values(ascending=False).head(5)
top_5_emissions_sources_excluding_missing

# Extracting the top 5 emission sources and their values
top_5_emission_sources = {
    "Transport Total": 194372.61,
    "Domestic Total": 177573.77,
    "Industry Total": 132556.21,
    "Road Transport (A roads)": 72630.97,
    "Domestic Electricity": 67037.06
}

# Plotting the top 5 emission sources
plt.figure(figsize=(12, 7))
plt.bar(top_5_emission_sources.keys(), top_5_emission_sources.values(), color=['blue', 'green', 'red', 'cyan', 'yellow'])
plt.xlabel('Emission Sources')
plt.ylabel('Emissions (kt CO₂e)')
plt.title('Top 5 Emission Sources in West Midlands (2005-2021)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# Displaying the column names of the dataset from sheet "1_2"
columns_1_2 = data_1_2_cleaned.columns.tolist()

# Filtering the dataset for only the West Midlands districts using the correct column name
west_midlands_data_1_2 = data_1_2_cleaned[data_1_2_cleaned['Local Authority'].isin(west_midlands_districts)]

# Grouping by year and summing up the emissions for all West Midlands districts
west_midlands_yearly_emissions = west_midlands_data_1_2.groupby('Calendar Year')['Grand Total'].sum().reset_index()

# Displaying the yearly emissions for the West Midlands districts
west_midlands_yearly_emissions

# Directly using the known columns for emission sources
emission_columns = [
    "Industry Electricity", "Industry Gas ", "Large Industrial Installations", "Industry 'Other'", "Industry Total",
    "Commercial Electricity", "Commercial Gas ", "Commercial 'Other'", "Commercial Total",
    "Public Sector Electricity", "Public Sector Gas ", "Public Sector 'Other'", "Public Sector Total",
    "Domestic Electricity", "Domestic Gas ", "Domestic 'Other'", "Domestic Total",
    "Road Transport (A roads)", "Road Transport (Motorways)", "Road Transport (Minor roads)", "Diesel Railways", 
    "Transport 'Other'", "Transport Total", "Net Emissions: Forest land", "Net Emissions: Cropland",
    "Net Emissions: Grassland", "Net Emissions: Wetlands", "Net Emissions: Settlements", 
    "Net Emissions: Harvested Wood Products", "Net Emissions: Indirect N2O", "LULUCF Net Emissions",
    "Agriculture Electricity", "Agriculture Gas ", "Agriculture 'Other'", "Agriculture Livestock", 
    "Agriculture Soils", "Agriculture Total", "Landfill", "Waste Management 'Other'", "Waste Management Total"
]

# Sum the emissions across all years for each emission column

# Calculating the sum of emissions for each emission source across all years for West Midlands districts
emission_sums = west_midlands_data_1_2[emission_columns].sum().sort_values(ascending=False)

# Displaying the top 5 emission sources
top_5_emission_sources = emission_sums.head(5)
top_5_emission_sources

# Removing any trailing spaces from the emission columns list
cleaned_emission_columns = [col.strip() for col in emission_columns]

# Calculating the sum of emissions for each emission source across all years for West Midlands districts
emission_sums = west_midlands_data_1_2[cleaned_emission_columns].sum().sort_values(ascending=False)

# Displaying the top 5 emission sources
top_5_emission_sources = emission_sums.head(5)
top_5_emission_sources

# Checking the columns in the dataset to find the correct names
matching_columns = [col for col in data_1_2_cleaned.columns if any(emission in col for emission in cleaned_emission_columns)]
non_matching_columns = set(cleaned_emission_columns) - set(matching_columns)

matching_columns, non_matching_columns

# Correcting the column names
corrected_emission_columns = [col if col not in non_matching_columns else col + " " for col in cleaned_emission_columns]

# Calculating the sum of emissions for each emission source across all years for West Midlands districts
emission_sums = west_midlands_data_1_2[corrected_emission_columns].sum().sort_values(ascending=False)

# Displaying the top 5 emission sources
top_5_emission_sources = emission_sums.head(5)
top_5_emission_sources

# Grouping by "Calendar Year" and summing the emissions for top 5 sections
columns_of_interest = ["Transport Total", "Domestic Total", "Industry Total", "Domestic Gas", "Road Transport (A roads)"]
yearly_emissions = west_midlands_data_1_2.groupby('Calendar Year')[columns_of_interest].sum()



# Plotting the trend for the top 5 sections
yearly_emissions.plot(figsize=(14, 7), marker='o')
plt.title("Yearly Emissions Trend for Top 5 Sections in West Midlands")
plt.ylabel("Emissions (kt CO₂e)")
plt.xlabel("Year")
plt.grid(True)
plt.legend(loc="upper right")
plt.tight_layout()
plt.show()

# Loading the dataset from sheet "1_3"
data_1_3_cleaned = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name="1_3", skiprows=4)

# Filtering the dataset for only the West Midlands districts using the correct column name
west_midlands_data_1_3 = data_1_3_cleaned[data_1_3_cleaned['Local Authority'].isin(west_midlands_districts)]

# Grouping by year and summing up the emissions for all West Midlands districts
west_midlands_yearly_emissions_methane = west_midlands_data_1_3.groupby('Calendar Year')['Grand Total'].sum().reset_index()

# Displaying the yearly methane emissions for the West Midlands districts
west_midlands_yearly_emissions_methane

# List of emission columns
emission_columns = [
    "Industry Electricity", "Industry Gas", "Large Industrial Installations", "Industry 'Other'", "Industry Total",
    "Commercial Electricity", "Commercial Gas", "Commercial 'Other'", "Commercial Total",
    "Public Sector Electricity", "Public Sector Gas", "Public Sector 'Other'", "Public Sector Total",
    "Domestic Electricity", "Domestic Gas", "Domestic 'Other'", "Domestic Total",
    "Road Transport (A roads)", "Road Transport (Motorways)", "Road Transport (Minor roads)", "Diesel Railways", 
    "Transport 'Other'", "Transport Total", "Net Emissions: Forest land", "Net Emissions: Cropland",
    "Net Emissions: Grassland", "Net Emissions: Wetlands", "Net Emissions: Settlements", 
    "Net Emissions: Harvested Wood Products", "Net Emissions: Indirect N2O", "LULUCF Net Emissions",
    "Agriculture Electricity", "Agriculture Gas", "Agriculture 'Other'", "Agriculture Livestock", 
    "Agriculture Soils", "Agriculture Total", "Landfill", "Waste Management 'Other'", "Waste Management Total"
]

# Summing emissions across all years for each emission column
emissions_total = west_midlands_data_1_3[emission_columns].sum()

# Getting the top 5 emission sources based on the total emissions
top_5_emissions_sources = emissions_total.nlargest(5)
top_5_emissions_sources

# Checking the columns in the dataset to identify any discrepancies
[col for col in west_midlands_data_1_3.columns if 'Gas' in col]

# Adjusting the column names based on the actual names in the dataset
adjusted_emission_columns = [
    "Industry Electricity", "Industry Gas ", "Large Industrial Installations", "Industry 'Other'", "Industry Total",
    "Commercial Electricity", "Commercial Gas ", "Commercial 'Other'", "Commercial Total",
    "Public Sector Electricity", "Public Sector Gas ", "Public Sector 'Other'", "Public Sector Total",
    "Domestic Electricity", "Domestic Gas", "Domestic 'Other'", "Domestic Total",
    "Road Transport (A roads)", "Road Transport (Motorways)", "Road Transport (Minor roads)", "Diesel Railways", 
    "Transport 'Other'", "Transport Total", "Net Emissions: Forest land", "Net Emissions: Cropland",
    "Net Emissions: Grassland", "Net Emissions: Wetlands", "Net Emissions: Settlements", 
    "Net Emissions: Harvested Wood Products", "Net Emissions: Indirect N2O", "LULUCF Net Emissions",
    "Agriculture Electricity", "Agriculture Gas", "Agriculture 'Other'", "Agriculture Livestock", 
    "Agriculture Soils", "Agriculture Total", "Landfill", "Waste Management 'Other'", "Waste Management Total"
]

# Summing emissions across all years for each emission column using the adjusted column names
adjusted_emissions_total = west_midlands_data_1_3[adjusted_emission_columns].sum()

# Getting the top 5 emission sources based on the total emissions
top_5_adjusted_emissions_sources = adjusted_emissions_total.nlargest(5)
top_5_adjusted_emissions_sources

# Summing the emissions across all years for each emission column
emission_totals_1_3 = west_midlands_data_1_3[emission_columns].sum()

# Extracting the top 5 emission sources
top_5_sources_1_3 = emission_totals_1_3.nlargest(5).index
top_5_emissions_1_3 = west_midlands_data_1_3.groupby("Calendar Year")[top_5_sources_1_3].sum()

# Plotting the top 5 emission sources
top_5_emissions_1_3.plot(figsize=(14, 7), title="Top 5 Emission Sources in West Midlands (2005-2021)")
plt.ylabel("Emissions (kt CO₂e)")
plt.grid(True)
plt.legend(loc="upper right")
plt.tight_layout()
plt.show()

# Correcting the columns for sheet "1_3"
corrected_emission_columns = [col for col in emission_columns if col in west_midlands_data_1_3.columns]

# Summing the emissions across all years for each corrected emission column
emission_totals_1_3_corrected = west_midlands_data_1_3[corrected_emission_columns].sum()

# Extracting the top 5 emission sources
top_5_sources_1_3_corrected = emission_totals_1_3_corrected.nlargest(5).index
top_5_emissions_1_3_corrected = west_midlands_data_1_3.groupby("Calendar Year")[top_5_sources_1_3_corrected].sum()

# Plotting the top 5 emission sources
top_5_emissions_1_3_corrected.plot(figsize=(14, 7), title="Top 5 Methane Emission Sources in West Midlands (2005-2021) from sheet '1_3'", marker='o')
plt.ylabel("Emissions (kt CO₂e)")
plt.grid(True)
plt.legend(loc="upper right")
plt.tight_layout()
plt.show()

# Summing the emissions across all years for each emission column and then getting the least 5
least_5_emissions = west_midlands_data_1_3[emission_columns].sum().nsmallest(5)

# Displaying the least 5 emission sources
least_5_emissions

# Fixing the column names by removing the extra spaces
corrected_emission_columns = [col.strip() for col in emission_columns]

# Summing the emissions across all years for each emission column and then getting the least 5
least_5_emissions = west_midlands_data_1_3[corrected_emission_columns].sum().nsmallest(5)

# Displaying the least 5 emission sources
least_5_emissions

# Checking the available columns in the dataframe to identify the discrepancies in the names
available_columns = west_midlands_data_1_3.columns
discrepancy_columns = [col for col in corrected_emission_columns if col not in available_columns]

# Displaying the columns causing discrepancies
discrepancy_columns

# Checking the column names in the dataframe that are closest to the discrepancy columns
potential_matches = {discrepancy: [col for col in available_columns if discrepancy.split()[0] in col] for discrepancy in discrepancy_columns}

# Displaying the potential matches for the discrepancy columns
potential_matches

# Correcting the names in the emission columns list
corrected_emission_columns = [col if col not in discrepancy_columns else col + ' ' for col in corrected_emission_columns]

# Summing the emissions across all years for each emission column and then getting the least 5
least_5_emissions = west_midlands_data_1_3[corrected_emission_columns].sum().nsmallest(5)

# Displaying the least 5 emission sources
least_5_emissions

# Plotting the five sources with the least emissions
least_emission_sources = least_5_emissions.index.tolist()
least_emissions_data = west_midlands_data_1_3.groupby('Calendar Year')[least_emission_sources].sum()

plt.figure(figsize=(15, 8))
for source in least_emission_sources:
    plt.plot(least_emissions_data.index, least_emissions_data[source], label=source, marker='o')

plt.title("Least 5 Methane Emission Sources in West Midlands (2005-2021)")
plt.xlabel("Year")
plt.ylabel("Emissions (kt CO2e)")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

import tensorflow as tf

# Assuming X_train is your training data
m, n_x = X_train.shape

# Initialize weights and biases for each layer
W1 = tf.Variable(tf.random.normal([25, 64]))
b1 = tf.Variable(tf.zeros([25, 1]))

W2 = tf.Variable(tf.random.normal([15, 25]))
b2 = tf.Variable(tf.zeros([15, 1]))

W3 = tf.Variable(tf.random.normal([1, 15]))
b3 = tf.Variable(tf.zeros([1, 1]))

# Forward Propagation
def forward_propagation(X):
    Z1 = tf.matmul(W1, X) + b1
    A1 = tf.sigmoid(Z1)
    
    Z2 = tf.matmul(W2, A1) + b2
    A2 = tf.sigmoid(Z2)
    
    Z3 = tf.matmul(W3, A2) + b3
    A3 = tf.sigmoid(Z3)
    
    return A3

# Assuming y_train is your training labels
y_hat = forward_propagation(X_train.T)

import matplotlib.pyplot as plt

# Plotting the CO2 emissions data for better visualization
plt.figure(figsize=(16, 10))

# Plotting each emission source over the years
for column in co2_emissions_data.columns[1:]:
    plt.plot(co2_emissions_data['Year'], co2_emissions_data[column], label=column, marker='o')

plt.title('CO2 Emissions Trend in West Midlands (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.legend(loc='upper right')
plt.grid(True)
plt.tight_layout()
plt.show()

# Load the Excel file and list all sheet names
xls = pd.ExcelFile("visualization of the GreenHouseEmissions.xls")
sheet_names = xls.sheet_names

sheet_names

# Import necessary libraries and try to load the Excel file again
import pandas as pd

# Load the Excel file and list all sheet names
xls = pd.ExcelFile("visualization of the GreenHouseEmissions.xls")
sheet_names = xls.sheet_names

sheet_names

# Load the data from the "CO2 Emissions westmidlands" sheet
co2_emissions_data = xls.parse('CO2 Emissions westmidlands')

# Display the first few rows of the CO2 emissions data
co2_emissions_data.head()

# Plotting the CO2 emissions data for better visualization
plt.figure(figsize=(16, 10))

# Plotting each emission source over the years
for column in co2_emissions_data.columns[1:]:
    plt.plot(co2_emissions_data['Year'], co2_emissions_data[column], label=column, marker='o')

plt.title('CO2 Emissions Trend in West Midlands (2005-2021)')
plt.xlabel('Year')
plt.ylabel('Emissions (kt CO2e)')
plt.legend(loc='upper right')
plt.grid(True)
plt.tight_layout()
plt.show()

import pandas as pd

# Load the dataset from the provided Excel file
data = pd.read_excel("visualization of the GreenHouseEmissions.xls")

# Display the first few rows of the dataset to understand its structure
data.head()

import matplotlib.pyplot as plt

# Plotting the "Transport Total" emissions over the years
plt.figure(figsize=(12, 6))
plt.plot(data['Year'], data['Sum of Transport Total'], marker='o', linestyle='-')
plt.title('Transport Total Emissions Over Time')
plt.xlabel('Year')
plt.ylabel('Emissions')
plt.grid(True)
plt.tight_layout()
plt.show()

import pandas as pd

# Load the dataset from the provided Excel file
data = pd.read_excel("visualization of the GreenHouseEmissions.xls")

# Display the first few rows of the dataset to understand its structure
data.head()

import matplotlib.pyplot as plt

# Plotting the "Transport Total" emissions over the years
plt.figure(figsize=(12, 6))
plt.plot(data['Year'], data['Sum of Transport Total'], marker='o', linestyle='-')
plt.title('Transport Total Emissions Over Time')
plt.xlabel('Year')
plt.ylabel('Emissions')
plt.grid(True)
plt.tight_layout()
plt.show()

from statsmodels.tsa.stattools import adfuller

# Augmented Dickey-Fuller test
result = adfuller(data['Sum of Transport Total'])
adf_statistic = result[0]
p_value = result[1]

adf_statistic, p_value

# First order differencing
data['Transport_Diff'] = data['Sum of Transport Total'].diff()

# Plotting the differenced data
plt.figure(figsize=(12, 6))
plt.plot(data['Year'][1:], data['Transport_Diff'][1:], marker='o', linestyle='-')
plt.title('First Order Differenced Transport Total Emissions')
plt.xlabel('Year')
plt.ylabel('Differenced Emissions')
plt.grid(True)
plt.tight_layout()
plt.show()

# Augmented Dickey-Fuller test on the differenced data
result_diff = adfuller(data['Transport_Diff'].dropna())  # Removing NaN resulted from differencing
adf_statistic_diff = result_diff[0]
p_value_diff = result_diff[1]

adf_statistic_diff, p_value_diff

# Second order differencing
data['Transport_Diff_2'] = data['Transport_Diff'].diff()

# Plotting the second order differenced data
plt.figure(figsize=(12, 6))
plt.plot(data['Year'][2:], data['Transport_Diff_2'][2:], marker='o', linestyle='-')
plt.title('Second Order Differenced Transport Total Emissions')
plt.xlabel('Year')
plt.ylabel('Differenced Emissions')
plt.grid(True)
plt.tight_layout()
plt.show()

# Augmented Dickey-Fuller test on the second order differenced data
result_diff_2 = adfuller(data['Transport_Diff_2'].dropna())  # Removing NaN resulted from differencing
adf_statistic_diff_2 = result_diff_2[0]
p_value_diff_2 = result_diff_2[1]

adf_statistic_diff_2, p_value_diff_2

# Plotting ACF and PACF with fewer lags
fig, ax = plt.subplots(1, 2, figsize=(15, 4))

# ACF plot
plot_acf(data['Transport_Diff_2'].dropna(), ax=ax[0], lags=6)
ax[0].set_title('Autocorrelation Function (ACF)')

# PACF plot
plot_pacf(data['Transport_Diff_2'].dropna(), ax=ax[1], lags=6)
ax[1].set_title('Partial Autocorrelation Function (PACF)')

plt.tight_layout()
plt.show()

import pandas as pd
from statsmodels.tsa.arima.model import ARIMA

# Reload the dataset
data = pd.read_excel("visualization of the GreenHouseEmissions.xls")

# Define the revised p, d, q range for grid search
p_range = range(0, 3)
d_range = [2]  # Only using d=2 based on our earlier analysis
q_range = range(0, 3)

# Store results
results_summary_revised = []

# Grid search
for p in p_range:
    for d in d_range:
        for q in q_range:
            try:
                model = ARIMA(data['Sum of Transport Total'], order=(p, d, q))
                result = model.fit()
                results_summary_revised.append((p, d, q, result.aic, result.bic))
            except:
                continue

# Convert results into a DataFrame for easy viewing
results_df_revised = pd.DataFrame(results_summary_revised, columns=['p', 'd', 'q', 'AIC', 'BIC'])

# Sort by AIC (Akaike Information Criterion)
results_df_revised = results_df_revised.sort_values(by='AIC')

results_df_revised.head()

# Fit the ARIMA(0,2,0) model
model_020 = ARIMA(data['Sum of Transport Total'], order=(0, 2, 0))
results_020 = model_020.fit()

# Summary of the ARIMA(0,2,0) model
summary_020 = results_020.summary()
summary_020

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Extract residuals
residuals = results_020.resid

# Plot residuals
plt.figure(figsize=(15, 6))
plt.subplot(1, 2, 1)
residuals.plot(title="Residuals")
plt.xlabel('Year')
plt.ylabel('Residual value')

# Histogram of residuals
plt.subplot(1, 2, 2)
residuals.hist(bins=10)
plt.title("Histogram of Residuals")
plt.xlabel('Residual value')
plt.ylabel('Frequency')

plt.tight_layout()
plt.show()

# ACF and PACF of residuals
fig, ax = plt.subplots(1, 2, figsize=(15, 4))

# ACF plot
plot_acf(residuals, ax=ax[0], lags=10)
ax[0].set_title('Autocorrelation Function (ACF) of Residuals')

# PACF plot
plot_pacf(residuals, ax=ax[1], lags=10)
ax[1].set_title('Partial Autocorrelation Function (PACF) of Residuals')

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Plot residuals
plt.figure(figsize=(15, 6))
plt.subplot(1, 2, 1)
residuals.plot(title="Residuals")
plt.xlabel('Year')
plt.ylabel('Residual value')

# Histogram of residuals
plt.subplot(1, 2, 2)
residuals.hist(bins=10)
plt.title("Histogram of Residuals")
plt.xlabel('Residual value')
plt.ylabel('Frequency')

plt.tight_layout()
plt.show()

# ACF and PACF of residuals
fig, ax = plt.subplots(1, 2, figsize=(15, 4))

# ACF plot
plot_acf(residuals, ax=ax[0], lags=10)
ax[0].set_title('Autocorrelation Function (ACF) of Residuals')

# PACF plot
plot_pacf(residuals, ax=ax[1], lags=10)
ax[1].set_title('Partial Autocorrelation Function (PACF) of Residuals')

plt.tight_layout()
plt.show()

# ACF and PACF of residuals with fewer lags
fig, ax = plt.subplots(1, 2, figsize=(15, 4))

# ACF plot
plot_acf(residuals, ax=ax[0], lags=6)
ax[0].set_title('Autocorrelation Function (ACF) of Residuals')

# PACF plot
plot_pacf(residuals, ax=ax[1], lags=6)
ax[1].set_title('Partial Autocorrelation Function (PACF) of Residuals')

plt.tight_layout()
plt.show()

import pandas as pd
# Retry loading the first few rows from the '1_1' sheet to explore its contents
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')
data_1_1.head()

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]

# Set the column names from the header row
data_1_1_actual.columns = data_1_1.iloc[3]

# Reset the index for the actual data
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Display the shape of the data
data_shape = data_1_1_actual.shape

# Get a summary of the data
data_summary = data_1_1_actual.describe()

# Check for missing values
missing_values = data_1_1_actual.isnull().sum()

data_shape, data_summary.head(), missing_values.head()

# Filter the data for the "West Midlands" Region/Country
west_midlands_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'West Midlands']

west_midlands_data.head()

import pandas as pd


# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]
data_1_1_actual.columns = data_1_1.iloc[3]
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Filter the data for the "West Midlands" Region/Country
west_midlands_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'West Midlands']

west_midlands_data

#west_midlands_data.to_csv('west_midlands_data.csv')

# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]
data_1_1_actual.columns = data_1_1.iloc[3]
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Filter the data for the "West Midlands" Region/Country
west_midlands_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'West Midlands']

# Filter out rows where "Second Tier Authority" ends with "Total"
west_midlands_data_filtered = west_midlands_data[~west_midlands_data['Second Tier Authority'].str.endswith("Total")]

west_midlands_data_filtered

# List of columns to keep
columns_to_keep = ['Region/Country', 'Second Tier Authority', 'Local Authority','Local Authority Code', 'Calendar Year', 'LULUCF Net Emissions']

# Columns that end with "Total"
total_columns = [col for col in west_midlands_data_filtered.columns if col.endswith("Total")]

# Combine both lists
columns_to_keep += total_columns

# Filter the dataset for these columns
west_midlands_filtered_columns = west_midlands_data_filtered[columns_to_keep]

west_midlands_filtered_columns

import pandas as pd


# Reload the data from the '1_1' sheet
data_1_1 = pd.read_excel("2005-21-uk-local-authority-ghg-emissions-update-060723.xlsx", sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]
data_1_1_actual.columns = data_1_1.iloc[3]
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Filter the data for the "West Midlands" Region/Country
west_midlands_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'West Midlands']

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]
data_1_1_actual.columns = data_1_1.iloc[3]
data_1_1_actual = data_1_1_actual.reset_index(drop=True)

# Filter the data for the "West Midlands" Region/Country
west_midlands_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'West Midlands']

# Filter out rows where "Second Tier Authority" ends with "Total"
west_midlands_data_filtered = west_midlands_data[~west_midlands_data['Second Tier Authority'].str.endswith("Total")]

# List of columns to keep
columns_to_keep = ['Region/Country', 'Second Tier Authority', 'Local Authority','Local Authority Code', 'Calendar Year', 'LULUCF Net Emissions']

# Columns that end with "Total"
total_columns = [col for col in west_midlands_data_filtered.columns if col.endswith("Total")]

# Combine both lists
columns_to_keep += total_columns

# Filter the dataset for these columns
west_midlands_filtered_columns = west_midlands_data_filtered[columns_to_keep]

west_midlands_filtered_columns

import pandas as pd

# Load the data from the provided URL
url = 'https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1166194/2005-21-uk-local-authority-ghg-emissions.xlsx'
data_1_1 = pd.read_excel(url, sheet_name='1_1')

# Extract the actual data, excluding the header information
data_1_1_actual = data_1_1.iloc[4:]
data_1_1_actual.columns = data_1_1.iloc[3]
data_1_1_actual.reset_index(drop=True, inplace=True)

# Filter the data for the "West Midlands" Region/Country
west_midlands_data = data_1_1_actual[data_1_1_actual['Region/Country'] == 'West Midlands']

# Filter out rows where "Second Tier Authority" ends with "Total"
west_midlands_data_filtered = west_midlands_data[~west_midlands_data['Second Tier Authority'].str.endswith("Total")]

# List of columns to keep
columns_to_keep = ['Region/Country', 'Second Tier Authority', 'Local Authority', 'Local Authority Code', 'Calendar Year', 'LULUCF Net Emissions']

# Add columns that end with "Total"
columns_to_keep.extend([col for col in west_midlands_data_filtered.columns if col.endswith("Total")])

# Filter the dataset for these columns
west_midlands_filtered_columns = west_midlands_data_filtered[columns_to_keep]

west_midlands_filtered_columns

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
4	North East	Darlington	Darlington	E06000005	2005	51.87311	114.701874	0.045681	43.073543	209.694209	...	12.699185	63.7069	34.256022	5.613394	39.869415	968.661604	100.287	9.658895	197.4758	4.905217
5	North East	Darlington	Darlington	E06000005	2006	55.398988	97.614091	0.065836	42.086928	195.165842	...	11.659759	59.927177	31.802024	6.57408	38.376104	943.627506	101.509	9.295998	197.4758	4.778446
6	North East	Darlington	Darlington	E06000005	2007	52.249398	95.167732	0.074503	43.79521	191.286843	...	12.166507	59.716732	37.048534	6.81813	43.866664	925.275164	102.632	9.015465	197.4758	4.685512
7	North East	Darlington	Darlington	E06000005	2008	51.651166	95.266031	0.055964	35.982972	182.956132	...	12.55795	59.566044	7.662955	6.771385	14.43434	876.045542	103.694	8.448373	197.4758	4.436217
8	North East	Darlington	Darlington	E06000005	2009	45.607413	82.045964	1.061014	26.863721	155.578112	...	11.405542	57.689627	16.001037	6.053694	22.054731	805.592471	104.355	7.71973	197.4758	4.079449
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7105	National Total	National Total	National Total	NaN	2017	18256.184881	15602.265158	33619.941766	17990.618473	85469.010278	...	9689.245642	51301.203756	15821.005923	5131.240623	20952.246546	437824.383087	66067.257699	6.62695	248717.5706	1.760328
7106	National Total	National Total	National Total	NaN	2018	20997.236163	19161.407779	32309.811618	17713.459471	90181.915031	...	9573.565596	51380.522611	15911.984565	5088.278772	21000.263337	430745.509283	66371.006647	6.489965	248717.5706	1.731866
7107	National Total	National Total	National Total	NaN	2019	18512.578663	18433.106892	31951.4012	17364.788398	86261.875153	...	9770.415392	50706.546836	15684.547926	5035.738233	20720.286159	416856.663324	66769.633181	6.243207	248717.5706	1.676024
7108	National Total	National Total	National Total	NaN	2020	15164.001049	17463.024978	30135.710515	17584.10729	80346.843833	...	8890.946812	49220.899615	14304.397217	4915.16683	19219.564047	376807.810496	67044.605507	5.620255	248717.5706	1.515003
7109	National Total	National Total	National Total	NaN	2021	17109.121337	20037.052043	29267.622916	17927.304689	84341.100985	...	9247.865828	50711.980197	13618.050957	5195.505715	18813.556673	399046.140782	67026.307	5.953575	248717.5706	1.604415

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
0	North East	Darlington	Darlington	E06000005	2005	51.87311	114.701874	0.045681	43.073543	209.694209	...	12.699185	63.7069	34.256022	5.613394	39.869415	968.661604	100.287	9.658895	197.4758	4.905217
1	North East	Darlington	Darlington	E06000005	2006	55.398988	97.614091	0.065836	42.086928	195.165842	...	11.659759	59.927177	31.802024	6.57408	38.376104	943.627506	101.509	9.295998	197.4758	4.778446
2	North East	Darlington	Darlington	E06000005	2007	52.249398	95.167732	0.074503	43.79521	191.286843	...	12.166507	59.716732	37.048534	6.81813	43.866664	925.275164	102.632	9.015465	197.4758	4.685512
3	North East	Darlington	Darlington	E06000005	2008	51.651166	95.266031	0.055964	35.982972	182.956132	...	12.55795	59.566044	7.662955	6.771385	14.43434	876.045542	103.694	8.448373	197.4758	4.436217
4	North East	Darlington	Darlington	E06000005	2009	45.607413	82.045964	1.061014	26.863721	155.578112	...	11.405542	57.689627	16.001037	6.053694	22.054731	805.592471	104.355	7.71973	197.4758	4.079449
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7101	National Total	National Total	National Total	NaN	2017	18256.184881	15602.265158	33619.941766	17990.618473	85469.010278	...	9689.245642	51301.203756	15821.005923	5131.240623	20952.246546	437824.383087	66067.257699	6.62695	248717.5706	1.760328
7102	National Total	National Total	National Total	NaN	2018	20997.236163	19161.407779	32309.811618	17713.459471	90181.915031	...	9573.565596	51380.522611	15911.984565	5088.278772	21000.263337	430745.509283	66371.006647	6.489965	248717.5706	1.731866
7103	National Total	National Total	National Total	NaN	2019	18512.578663	18433.106892	31951.4012	17364.788398	86261.875153	...	9770.415392	50706.546836	15684.547926	5035.738233	20720.286159	416856.663324	66769.633181	6.243207	248717.5706	1.676024
7104	National Total	National Total	National Total	NaN	2020	15164.001049	17463.024978	30135.710515	17584.10729	80346.843833	...	8890.946812	49220.899615	14304.397217	4915.16683	19219.564047	376807.810496	67044.605507	5.620255	248717.5706	1.515003
7105	National Total	National Total	National Total	NaN	2021	17109.121337	20037.052043	29267.622916	17927.304689	84341.100985	...	9247.865828	50711.980197	13618.050957	5195.505715	18813.556673	399046.140782	67026.307	5.953575	248717.5706	1.604415

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
0	North East	Darlington	Darlington	E06000005	2005	51.87311	114.701874	0.045681	43.073543	209.694209	...	12.699185	63.7069	34.256022	5.613394	39.869415	968.661604	100.287	9.658895	197.4758	4.905217
1	North East	Darlington	Darlington	E06000005	2006	55.398988	97.614091	0.065836	42.086928	195.165842	...	11.659759	59.927177	31.802024	6.57408	38.376104	943.627506	101.509	9.295998	197.4758	4.778446
2	North East	Darlington	Darlington	E06000005	2007	52.249398	95.167732	0.074503	43.79521	191.286843	...	12.166507	59.716732	37.048534	6.81813	43.866664	925.275164	102.632	9.015465	197.4758	4.685512
3	North East	Darlington	Darlington	E06000005	2008	51.651166	95.266031	0.055964	35.982972	182.956132	...	12.55795	59.566044	7.662955	6.771385	14.43434	876.045542	103.694	8.448373	197.4758	4.436217
4	North East	Darlington	Darlington	E06000005	2009	45.607413	82.045964	1.061014	26.863721	155.578112	...	11.405542	57.689627	16.001037	6.053694	22.054731	805.592471	104.355	7.71973	197.4758	4.079449
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7101	National Total	National Total	National Total	NaN	2017	18256.184881	15602.265158	33619.941766	17990.618473	85469.010278	...	9689.245642	51301.203756	15821.005923	5131.240623	20952.246546	437824.383087	66067.257699	6.62695	248717.5706	1.760328
7102	National Total	National Total	National Total	NaN	2018	20997.236163	19161.407779	32309.811618	17713.459471	90181.915031	...	9573.565596	51380.522611	15911.984565	5088.278772	21000.263337	430745.509283	66371.006647	6.489965	248717.5706	1.731866
7103	National Total	National Total	National Total	NaN	2019	18512.578663	18433.106892	31951.4012	17364.788398	86261.875153	...	9770.415392	50706.546836	15684.547926	5035.738233	20720.286159	416856.663324	66769.633181	6.243207	248717.5706	1.676024
7104	National Total	National Total	National Total	NaN	2020	15164.001049	17463.024978	30135.710515	17584.10729	80346.843833	...	8890.946812	49220.899615	14304.397217	4915.16683	19219.564047	376807.810496	67044.605507	5.620255	248717.5706	1.515003
7105	National Total	National Total	National Total	NaN	2021	17109.121337	20037.052043	29267.622916	17927.304689	84341.100985	...	9247.865828	50711.980197	13618.050957	5195.505715	18813.556673	399046.140782	67026.307	5.953575	248717.5706	1.604415

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
2040	West Midlands	Birmingham	Birmingham	E08000025	2005	526.551509	366.037594	61.953269	261.620286	1216.162658	...	2.318744	15.128539	11.122149	43.552592	54.674741	7059.334372	1014.65	6.957408	267.7912	26.361338
2041	West Midlands	Birmingham	Birmingham	E08000025	2006	537.58464	325.085629	82.275024	254.186129	1199.131422	...	2.519812	15.762343	10.324532	48.716274	59.040806	6952.112034	1020.843	6.810168	267.7912	25.960943
2042	West Midlands	Birmingham	Birmingham	E08000025	2007	510.882763	311.246683	94.635887	259.93259	1176.697923	...	2.361162	15.055982	36.275553	50.047079	86.322633	6800.522322	1029.021	6.60873	267.7912	25.394869
2043	West Midlands	Birmingham	Birmingham	E08000025	2008	529.503396	305.588896	133.077044	207.353832	1175.523168	...	2.487657	14.784999	30.037779	48.617361	78.655141	6744.677640	1038.98	6.491634	267.7912	25.18633
2044	West Midlands	Birmingham	Birmingham	E08000025	2009	429.653608	281.346962	44.531193	182.413125	937.944889	...	2.289698	13.508004	159.413026	47.379221	206.792247	6099.737055	1050.072	5.808875	267.7912	22.777959
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2596	West Midlands	Worcestershire Total	Worcestershire Total	NaN	2017	152.467092	116.944652	19.834311	217.425383	506.671438	...	68.894191	386.871084	216.880509	46.866939	263.747448	3987.637970	596.365709	6.686565	1740.5141	2.291069
2597	West Midlands	Worcestershire Total	Worcestershire Total	NaN	2018	172.838333	131.012733	21.624796	232.232283	557.708145	...	69.275392	405.306105	194.41849	46.092127	240.510617	3895.210521	600.345089	6.488286	1740.5141	2.237966
2598	West Midlands	Worcestershire Total	Worcestershire Total	NaN	2019	146.809055	137.062654	19.014284	197.144455	500.030448	...	72.29286	383.175445	194.909071	47.087417	241.996488	3745.219387	604.075264	6.199922	1740.5141	2.151789
2599	West Midlands	Worcestershire Total	Worcestershire Total	NaN	2020	121.875589	115.848439	15.014089	197.552499	450.290616	...	60.056299	359.954182	193.621385	44.625131	238.246516	3293.630698	606.317326	5.43219	1740.5141	1.892332
2600	West Midlands	Worcestershire Total	Worcestershire Total	NaN	2021	141.541027	169.279094	18.323263	205.906984	535.050368	...	68.156731	373.633695	201.635026	46.49455	248.129576	3631.573462	604.947	6.003127	1740.5141	2.086495

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
2040	West Midlands	Birmingham	Birmingham	E08000025	2005	526.551509	366.037594	61.953269	261.620286	1216.162658	...	2.318744	15.128539	11.122149	43.552592	54.674741	7059.334372	1014.65	6.957408	267.7912	26.361338
2041	West Midlands	Birmingham	Birmingham	E08000025	2006	537.58464	325.085629	82.275024	254.186129	1199.131422	...	2.519812	15.762343	10.324532	48.716274	59.040806	6952.112034	1020.843	6.810168	267.7912	25.960943
2042	West Midlands	Birmingham	Birmingham	E08000025	2007	510.882763	311.246683	94.635887	259.93259	1176.697923	...	2.361162	15.055982	36.275553	50.047079	86.322633	6800.522322	1029.021	6.60873	267.7912	25.394869
2043	West Midlands	Birmingham	Birmingham	E08000025	2008	529.503396	305.588896	133.077044	207.353832	1175.523168	...	2.487657	14.784999	30.037779	48.617361	78.655141	6744.677640	1038.98	6.491634	267.7912	25.18633
2044	West Midlands	Birmingham	Birmingham	E08000025	2009	429.653608	281.346962	44.531193	182.413125	937.944889	...	2.289698	13.508004	159.413026	47.379221	206.792247	6099.737055	1050.072	5.808875	267.7912	22.777959
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2579	West Midlands	Worcestershire	Wyre Forest	E07000239	2017	21.596821	10.207297	1.131147	48.643531	81.578795	...	6.365818	33.125984	35.766842	5.064742	40.831584	499.390383	101.886605	4.901433	195.4038	2.555684
2580	West Midlands	Worcestershire	Wyre Forest	E07000239	2018	26.603644	13.005476	1.125587	59.088112	99.82282	...	6.132734	32.343872	32.129977	5.063163	37.19314	489.710354	102.301938	4.786912	195.4038	2.506145
2581	West Midlands	Worcestershire	Wyre Forest	E07000239	2019	23.235559	17.494639	1.147332	42.889724	84.767255	...	6.015508	31.772339	31.502365	4.954569	36.456934	458.909804	102.485512	4.477802	195.4038	2.34852
2582	West Midlands	Worcestershire	Wyre Forest	E07000239	2020	17.900403	11.514084	1.128091	44.760307	75.302885	...	5.358702	30.189129	31.393792	4.728242	36.122034	408.559610	102.162048	3.999133	195.4038	2.090848
2583	West Midlands	Worcestershire	Wyre Forest	E07000239	2021	21.503682	14.971111	1.077544	43.31466	80.866997	...	5.185539	30.605628	32.829161	5.01005	37.839211	436.541231	101.786	4.288814	195.4038	2.234047

West Midlands' Carbon Footprint

Understanding the Data¶

Process Flow

WestMidlands

Regularization

London

Wales

Per capita emissions refer to the average release of emissions (carbon dioxide, CO2) attributed to each individual within a certain population.

The term "Average Emissions per km² by Region" refers to a measure used to understand the density of emissions in a given area. This metric provides an average value of emissions (carbon dioxide, CO2) distributed over the total land area of a region, expressed in square kilometers (km²)

Per capita emissions refer to the average release of emissions (carbon dioxide, CO2) attributed to each individual within a certain population.

The term "Average Emissions per km² by Region" refers to a measure used to understand the density of emissions in a given area. This metric provides an average value of emissions (carbon dioxide, CO2) distributed over the total land area of a region, expressed in square kilometers (km²)

ARIMA

ARIMA(AutoRegressive Integrated Moving Average) Model Output Analysis

ARIMA Model Output Analysis

	Table 1.1: Local Authority territorial greenhouse gas emissions estimates 2005-2021 (kt CO2e) - Full dataset	Unnamed: 1	Unnamed: 2	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Unnamed: 9	...	Unnamed: 40	Unnamed: 41	Unnamed: 42	Unnamed: 43	Unnamed: 44	Unnamed: 45	Unnamed: 46	Unnamed: 47	Unnamed: 48	Unnamed: 49
0	This worksheet contains one table. The table c...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Freeze panes are active on this sheet. To turn...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	Filters are active in cells A5 to AX5 and may ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
4	North East	Darlington	Darlington	E06000005	2005	51.87311	114.701874	0.045681	43.073543	209.694209	...	12.699185	63.7069	34.256022	5.613394	39.869415	968.661604	100.287	9.658895	197.4758	4.905217

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
count	7106	7106	7106	6358	7106	7106.00000	7106.0	7106.0	7106.0	7106.000000	...	7106.0	7106.0	7106.0	7106.0	7106.0	7106.000000	7106.0	7106.0	7106.0	7106.0
unique	27	194	418	374	17	7106.00000	7002.0	6118.0	7073.0	7106.000000	...	7073.0	7073.0	7090.0	7073.0	7090.0	7106.000000	7050.0	7073.0	417.0	7073.0
top	South East	Scotland	Darlington	E06000005	2005	51.87311	0.0	0.0	0.0	209.694209	...	0.0	0.0	0.0	0.0	0.0	968.661604	0.0	0.0	0.0	0.0
freq	1190	544	17	17	418	1.00000	105.0	925.0	34.0	1.000000	...	34.0	34.0	17.0	34.0	17.0	1.000000	34.0	34.0	34.0	34.0

3	Agriculture Total	Commercial Total	Domestic Total	Emissions per km2 (kt CO2e)	Industry Total	LULUCF Net Emissions	Public Sector Total	Transport Total	Waste Management Total
Calendar Year
2005	4028.975542	5809.331253	13449.536094	289.942804	10110.465071	-317.593628	2157.052152	13501.32343	993.132504
2006	3977.611559	6076.632436	13583.317568	288.863353	10197.714268	-326.862871	2068.2373	13305.491246	982.898678
2007	3905.02243	5783.889127	13081.835599	286.449175	10138.235254	-364.168107	1940.546956	13427.290157	1704.174232
2008	3896.858302	5577.45716	13033.414716	274.077573	9265.801065	-409.084036	1836.585244	12933.307202	1555.034766
2009	3807.240767	4712.011264	11835.167963	252.942548	8063.486578	-410.381053	1569.812241	12494.989414	2215.670625
2010	3840.081371	5001.267074	12685.403118	265.708579	8605.356043	-416.001919	1702.472983	12409.032802	2569.207488
2011	3761.345066	4672.226863	11112.642004	244.269938	8053.840463	-437.639005	1543.437224	12275.012297	2099.16877
2012	3809.765776	4997.596854	11868.951211	250.674974	7934.255575	-405.366106	1655.926145	12166.688081	2004.062529
2013	3722.949528	4772.413248	11531.527193	245.219017	7930.368103	-444.5167	1632.970841	12070.626129	1965.555586
2014	3809.989355	4075.739643	9798.620535	226.299273	7683.166895	-443.113774	1407.210297	12321.712167	1760.41934
2015	3805.79878	3700.093353	9562.443689	221.343256	7258.445476	-467.135531	1343.820223	12586.051221	1781.099069
2016	3692.000037	3288.671641	9036.525528	211.633827	6871.729735	-419.905408	1185.686926	12802.317734	1976.630326
2017	3766.397313	2555.056076	8585.065579	205.073094	6795.989583	-447.164862	1247.662167	12775.515791	2203.734087
2018	3806.417076	1707.805299	8419.354441	199.917735	7338.841582	-432.569368	1400.427797	12562.288533	2002.438545
2019	3752.726686	1378.760231	8136.337663	189.296645	6823.598748	-457.906092	1289.674005	12222.766602	1934.145194
2020	3573.008128	1094.025601	7960.604044	169.355461	6329.377088	-461.502795	1210.227228	9941.294068	1725.606782
2021	3639.604571	1289.487328	8254.300582	181.918944	7025.526089	-458.060889	1333.317247	11009.606016	1643.074363

3	Agriculture Total	Commercial Total	Domestic Total	Emissions per km2 (kt CO2e)	Industry Total	LULUCF Net Emissions	Public Sector Total	Transport Total	Waste Management Total
3
Agriculture Total	1.000000	0.775494	0.793451	0.860274	0.880980	0.833005	0.861581	0.852000	-0.477497
Commercial Total	0.775494	1.000000	0.959494	0.974504	0.866052	0.729578	0.847188	0.680485	-0.267074
Domestic Total	0.793451	0.959494	1.000000	0.983375	0.928145	0.796254	0.934487	0.608518	-0.313520
Emissions per km2 (kt CO2e)	0.860274	0.974504	0.983375	1.000000	0.942496	0.818817	0.925586	0.732963	-0.321745
Industry Total	0.880980	0.866052	0.928145	0.942496	1.000000	0.889868	0.975990	0.684668	-0.527468
LULUCF Net Emissions	0.833005	0.729578	0.796254	0.818817	0.889868	1.000000	0.887573	0.665688	-0.608115
Public Sector Total	0.861581	0.847188	0.934487	0.925586	0.975990	0.887573	1.000000	0.604193	-0.542619
Transport Total	0.852000	0.680485	0.608518	0.732963	0.684668	0.665688	0.604193	1.000000	-0.237519
Waste Management Total	-0.477497	-0.267074	-0.313520	-0.321745	-0.527468	-0.608115	-0.542619	-0.237519	1.000000

Year	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Coefficient	-0.164813	0.01567557	0.30325461	-0.30009949	0.24781068	0.32482985	-0.01417399	-0.26335244	-0.28176354	0.05105317	-0.09310551	0.08112285	0.73103766	0.12332474	-0.58931361	0.84632316

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
3502	London	Barking and Dagenham	Barking and Dagenham	E09000002	2005	131.684063	17.459426	66.870493	28.426324	244.440306	...	0.211348	1.239115	161.428095	7.486672	168.914767	1143.769451	166.275	6.878782	37.7992	30.259091
3503	London	Barking and Dagenham	Barking and Dagenham	E09000002	2006	139.895257	13.63688	66.154453	28.690501	248.377092	...	0.396191	1.344134	149.864148	8.481513	158.345662	1137.669474	167.157	6.805994	37.7992	30.097713
3504	London	Barking and Dagenham	Barking and Dagenham	E09000002	2007	140.487938	12.727459	69.810178	30.324736	253.350311	...	0.25175	1.199139	155.877849	8.75395	164.631799	1129.493177	169.031	6.682166	37.7992	29.881404
3505	London	Barking and Dagenham	Barking and Dagenham	E09000002	2008	135.419965	12.319912	68.498117	25.60148	241.839473	...	0.151626	0.907199	128.605453	8.869047	137.4745	1059.170470	172.452	6.141828	37.7992	28.020976
3506	London	Barking and Dagenham	Barking and Dagenham	E09000002	2009	115.972501	12.15477	59.188809	21.28696	208.603041	...	0.129006	0.841917	178.517672	8.801381	187.319052	1027.842511	177.58	5.788053	37.7992	27.192176
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4058	London	Westminster	Westminster	E09000033	2017	47.698762	23.283152	0.085511	95.767013	166.834438	...	0.017115	4.485572	9.338904	7.83116	17.170064	2062.990819	202.606251	10.182266	22.0301	93.644188
4059	London	Westminster	Westminster	E09000033	2018	131.422791	59.29191	1.014299	94.752952	286.481952	...	0.019147	15.027827	11.016105	7.773276	18.789381	1985.655839	201.909585	9.834381	22.0301	90.133764
4060	London	Westminster	Westminster	E09000033	2019	106.191379	57.354249	0.268476	84.414842	248.228947	...	0.021237	14.091068	9.521645	7.656918	17.178562	1768.356274	200.526526	8.818565	22.0301	80.270007
4061	London	Westminster	Westminster	E09000033	2020	69.206872	52.611429	0.188609	87.61706	209.62397	...	0.009882	14.423154	14.093663	7.487961	21.581624	1444.948575	200.752862	7.197649	22.0301	65.589742
4062	London	Westminster	Westminster	E09000033	2021	94.854934	69.663544	0.186295	107.401328	272.106101	...	0.040033	21.398514	4.464876	7.905342	12.370218	1671.932170	205.087	8.152307	22.0301	75.893081

3	Region/Country	Second Tier Authority	Local Authority	Local Authority Code	Calendar Year	Industry Electricity	Industry Gas	Large Industrial Installations	Industry 'Other'	Industry Total	...	Agriculture Soils	Agriculture Total	Landfill	Waste Management 'Other'	Waste Management Total	Grand Total	Population ('000s, mid-year estimate)	Per Capita Emissions (tCO2e)	Area (km2)	Emissions per km2 (kt CO2e)
5899	Wales	Wales	Blaenau Gwent	W06000019	2005	65.59808	30.923927	0.910503	31.863069	129.295578	...	1.393334	9.764239	101.06659	3.698073	104.764663	590.397163	69.188	8.533231	108.7279	5.430043
5900	Wales	Wales	Blaenau Gwent	W06000019	2006	68.297658	37.994917	0.882636	33.684962	140.860173	...	1.312323	9.408435	94.259989	4.270126	98.530116	594.764649	69.61	8.544241	108.7279	5.470212
5901	Wales	Wales	Blaenau Gwent	W06000019	2007	62.6949	35.219753	0.895169	32.121519	130.931341	...	1.306047	9.717945	99.165453	4.397067	103.56252	576.227727	69.685	8.269035	108.7279	5.299723
5902	Wales	Wales	Blaenau Gwent	W06000019	2008	66.289964	35.527274	0.850555	30.52934	133.197132	...	1.132106	9.145378	75.383486	4.401133	79.784619	559.320474	69.82	8.010892	108.7279	5.144222
5903	Wales	Wales	Blaenau Gwent	W06000019	2009	54.565764	24.645645	0.868735	30.739822	110.819967	...	1.198329	8.843912	66.026656	4.155314	70.18197	494.419940	69.85	7.07831	108.7279	4.547314
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6268	Wales	Wales	Wrexham	W06000006	2017	138.291306	215.451935	6.162685	58.279959	418.185885	...	24.874715	226.456449	38.64809	13.623222	52.271312	1296.506128	137.024688	9.461843	503.7739	2.573587
6269	Wales	Wales	Wrexham	W06000006	2018	124.951001	262.003868	6.784681	60.623925	454.363475	...	24.210341	219.62436	31.887202	14.560996	46.448198	1269.213023	137.113661	9.256649	503.7739	2.51941
6270	Wales	Wales	Wrexham	W06000006	2019	114.083503	227.250945	10.291972	58.155464	409.781884	...	23.919956	209.605174	36.983958	17.280628	54.264586	1206.602551	136.859706	8.816346	503.7739	2.395127
6271	Wales	Wales	Wrexham	W06000006	2020	87.7227	220.889613	8.987525	57.01724	374.617077	...	22.221266	229.931955	20.0322	16.058294	36.090495	1102.897206	136.391201	8.086278	503.7739	2.18927
6272	Wales	Wales	Wrexham	W06000006	2021	95.762223	296.66666	9.2927	60.24056	461.962144	...	23.31838	216.993751	20.752061	13.022652	33.774714	1207.502681	135.132	8.935727	503.7739	2.396914

3	Per Capita Emissions	Emissions per km^2
Region/Country
East Midlands	9.710848	5.318442
East Midlands Total	8.668612	2.514725
East of England	8.091197	5.769531
East of England Total	7.929507	2.398958
England Total	7.523039	3.022770
London	10.318067	47.155219
London Total	5.113713	26.209921
National Total	8.107857	2.072628
North East	11.400630	11.222972
North East Total	10.240767	3.060355
North West	9.260969	7.706417
North West Total	7.965210	3.794200
Northern Ireland	13.582651	3.095697
Northern Ireland Total	12.634336	1.605084
Scotland	11.779920	3.426134
Scotland Total	9.553573	0.630670
South East	6.804080	6.286119
South East Total	6.775691	3.038176
South West	8.215306	3.880700
South West Total	7.664305	1.685468
Unallocated	NaN	NaN
Wales	12.037397	4.060587
Wales Total	11.743721	1.691700
West Midlands	8.623030	7.402992
West Midlands Total	7.391437	3.215261
Yorkshire and the Humber	11.376461	5.033133
Yorkshire and the Humber Total	9.092107	3.103146

	Calendar Year	Commercial 'Other'	Net Emissions: Indirect N2O	Agriculture Gas	Per Capita Emissions (tCO2e)	Emissions per km2 (kt CO2e)
0	2005.0	0.6	0.2	0.4	9.7	4.9
1	2006.0	0.6	0.2	0.3	9.3	4.8
2	2007.0	0.6	0.2	0.3	9.0	4.7
3	2008.0	0.7	0.1	0.3	8.4	4.4
4	2009.0	0.6	0.1	0.3	7.7	4.1
...	...	...	...	...	...	...
7103	2019.0	347.0	152.4	813.6	6.2	1.7
7104	2020.0	191.7	152.8	923.3	5.6	1.5
7105	2021.0	223.1	152.3	903.4	6.0	1.6
7106	NaN	NaN	NaN	NaN	NaN	NaN
7107	NaN	NaN	NaN	NaN	NaN	NaN

	TSNE1	TSNE2
0	-23.196171	34.333500
1	15.931448	-3.052190
2	-9.544743	-12.004598
3	17.712883	-12.668040
4	23.051466	-1.983216
...	...	...
706	1.385328	-5.390362
707	-20.086830	15.288509
708	-13.126957	-15.810575
709	25.429058	1.221480
710	-11.236401	-8.841590

Calendar Year	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Calendar Year
2005	1.000000	0.999707	0.998732	0.998613	0.991479	0.991547	0.991912	0.993618	0.993859	0.983923	0.975687	0.961511	0.946135	0.939583	0.932480	0.947603	0.948853
2006	0.999707	1.000000	0.998403	0.998334	0.989789	0.990788	0.990035	0.992557	0.992489	0.980433	0.971191	0.955886	0.939172	0.931815	0.924216	0.940674	0.941725
2007	0.998732	0.998403	1.000000	0.998963	0.994393	0.994761	0.995211	0.995505	0.996049	0.987501	0.979535	0.966698	0.952439	0.945399	0.938189	0.951952	0.953161
2008	0.998613	0.998334	0.998963	1.000000	0.996148	0.996680	0.995657	0.997844	0.997708	0.986340	0.978960	0.965865	0.950809	0.941955	0.935568	0.950719	0.950351
2009	0.991479	0.989789	0.994393	0.996148	1.000000	0.998480	0.999338	0.999391	0.999718	0.993185	0.989331	0.980747	0.969569	0.959834	0.955375	0.966119	0.964785
2010	0.991547	0.990788	0.994761	0.996680	0.998480	1.000000	0.996746	0.998603	0.998446	0.986271	0.980466	0.969608	0.957166	0.947864	0.942720	0.958749	0.955883
2011	0.991912	0.990035	0.995211	0.995657	0.999338	0.996746	1.000000	0.998421	0.999245	0.996167	0.992308	0.984251	0.973556	0.964359	0.959465	0.967777	0.967906
2012	0.993618	0.992557	0.995505	0.997844	0.999391	0.998603	0.998421	1.000000	0.999808	0.990443	0.985482	0.975169	0.961938	0.950899	0.945959	0.958274	0.956731
2013	0.993859	0.992489	0.996049	0.997708	0.999718	0.998446	0.999245	0.999808	1.000000	0.992578	0.987924	0.978229	0.965972	0.956038	0.951104	0.962484	0.961484
2014	0.983923	0.980433	0.987501	0.986340	0.993185	0.986271	0.996167	0.990443	0.992578	1.000000	0.998836	0.994492	0.987349	0.980165	0.976078	0.977006	0.979746
2015	0.975687	0.971191	0.979535	0.978960	0.989331	0.980466	0.992308	0.985482	0.987924	0.998836	1.000000	0.998237	0.993195	0.985935	0.983098	0.980691	0.983483
2016	0.961511	0.955886	0.966698	0.965865	0.980747	0.969608	0.984251	0.975169	0.978229	0.994492	0.998237	1.000000	0.997802	0.990656	0.988950	0.981532	0.984991
2017	0.946135	0.939172	0.952439	0.950809	0.969569	0.957166	0.973556	0.961938	0.965972	0.987349	0.993195	0.997802	1.000000	0.996425	0.995866	0.986805	0.990171
2018	0.939583	0.931815	0.945399	0.941955	0.959834	0.947864	0.964359	0.950899	0.956038	0.980165	0.985935	0.990656	0.996425	1.000000	0.999545	0.993473	0.997028
2019	0.932480	0.924216	0.938189	0.935568	0.955375	0.942720	0.959465	0.945959	0.951104	0.976078	0.983098	0.988950	0.995866	0.999545	1.000000	0.993427	0.996302
2020	0.947603	0.940674	0.951952	0.950719	0.966119	0.958749	0.967777	0.958274	0.962484	0.977006	0.980691	0.981532	0.986805	0.993473	0.993427	1.000000	0.998725
2021	0.948853	0.941725	0.953161	0.950351	0.964785	0.955883	0.967906	0.956731	0.961484	0.979746	0.983483	0.984991	0.990171	0.997028	0.996302	0.998725	1.000000

Calendar Year	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Local Authority
Birmingham	7059.334372	6952.112034	6800.522322	6744.677640	6099.737055	6412.804284	5805.907414	6094.847957	5928.649539	5283.528982	5187.812112	5018.990855	4952.980766	4694.297182	4500.378294	4177.186378	4480.654084
Bromsgrove	956.642291	966.027098	1014.325992	966.508457	915.239428	943.043254	865.875640	885.439318	875.957613	847.917226	839.358149	828.019311	799.333806	765.570975	756.273104	655.139864	711.760256
Cannock Chase	588.979929	597.874966	584.212829	554.965633	534.431424	565.871617	516.643905	523.651492	521.293200	463.806795	448.224377	432.415392	438.056787	428.182712	400.378294	360.878619	364.042820
Coventry	2285.257172	2244.459643	2109.501041	2028.053404	1835.162506	1962.611578	1766.002096	1881.254900	1815.756821	1664.671871	1647.571233	1574.337656	1548.946359	1496.667926	1428.701726	1280.312165	1344.000349
Dudley	1983.590348	1972.154700	1888.505634	1818.345968	1657.763334	1778.665829	1603.028674	1645.915361	1619.695684	1452.515600	1412.477913	1356.141083	1347.120418	1295.213550	1217.885198	1079.756656	1163.033149
East Staffordshire	1287.270750	1298.775799	1260.546128	1245.328814	1170.588639	1228.732489	1160.773439	1165.877455	1143.246946	1050.973296	1014.291935	988.598271	981.240089	966.664142	903.141998	810.544121	853.521292
Herefordshire, County of	2091.684474	2090.207781	2038.853224	1975.986634	1872.500149	1994.484316	1849.438619	1892.331768	1847.560317	1777.587181	1696.464807	1660.690961	1618.084774	1598.639996	1527.132610	1364.626064	1472.865799
Lichfield	1004.513597	1022.789515	1013.242837	993.374843	955.231477	981.016859	931.692962	938.137617	935.001562	875.810423	863.532062	850.456232	851.408592	837.842706	813.911260	691.404937	754.890754
Malvern Hills	772.773159	799.621230	848.442583	797.397569	757.938462	782.645008	723.285139	726.094378	717.922272	695.654404	688.580938	676.822279	655.588099	642.098370	619.590085	538.434311	601.444378
Newcastle-under-Lyme	1121.822896	1113.654899	1107.267011	1087.895669	1042.573930	1083.119199	1024.749999	1024.635624	1021.924809	939.404697	969.297348	944.169090	929.997916	911.784056	875.110449	784.361909	844.190037
North Warwickshire	1147.478903	1203.041332	1226.193958	1194.492043	1121.940224	1191.290419	1085.180037	1110.955808	1077.930006	1065.117153	1060.260000	1029.729619	1025.527236	1001.285910	961.051901	823.867578	919.630410
Nuneaton and Bedworth	813.097075	815.719471	861.239859	828.813779	772.443801	765.017013	677.858160	704.027486	684.424974	638.996823	634.593829	606.356718	597.691045	593.546030	563.796985	505.938444	540.854821
Redditch	585.906245	591.548822	632.697674	593.920918	533.689582	577.553981	522.724477	528.282173	500.489462	468.059313	444.165840	421.000712	406.630410	394.836203	383.634496	332.680616	357.787785
Rugby	2330.213067	2373.288078	2651.893434	2411.372020	2278.320929	2283.729045	2334.076177	2124.141199	2157.994109	2133.777044	1993.474564	2109.935757	2000.143439	2041.847091	1988.209932	1853.518649	2026.454350
Sandwell	2289.664199	2311.288395	2271.410144	2149.609456	1915.683984	2035.995284	1862.670040	1942.089900	1917.089725	1748.667558	1785.651958	1577.895063	1587.577139	1534.233142	1453.591967	1315.105902	1401.088684
Shropshire	3788.039158	3772.811560	3701.754274	3605.369762	3330.136985	3537.762726	3277.426402	3379.256702	3297.401331	3129.205145	3092.862487	3029.587506	2980.901584	2986.369707	2882.366244	2645.075805	2773.370585
Solihull	1735.803608	1801.630633	1757.701820	1667.849693	1565.457992	1683.199281	1588.399097	1667.768397	1627.491397	1492.473058	1518.294020	1475.326158	1427.841084	1410.483026	1327.805102	1145.680549	1226.181599
South Staffordshire	1142.941882	1174.670664	1167.283617	1104.231784	1101.010671	1130.681369	1083.159234	1088.067517	1082.128759	1028.457756	1000.357801	1002.784932	1034.087269	1000.812994	973.519842	870.868301	954.939263
Stafford	1593.609395	1616.541260	1569.472252	1566.564484	1533.426323	1571.727852	1488.504814	1541.704997	1510.290365	1422.200740	1409.541939	1378.918704	1343.494927	1325.675278	1270.246497	1101.396010	1166.734395
Staffordshire Moorlands	1776.923545	1766.082186	1742.557987	1656.608649	1559.741550	1655.713572	1603.156284	1544.428454	1564.011891	1546.833793	1525.699941	1497.001650	1413.357126	1475.730299	1386.199936	1274.232906	1332.991944
Stoke-on-Trent	2041.728131	2025.362057	1977.856972	1900.940420	1777.333094	1764.352853	1581.980925	1584.479240	1577.177978	1606.524597	1550.265296	1507.331663	1261.650147	1223.080833	1153.929946	1020.164468	1127.356084
Stratford-on-Avon	1501.480094	1513.735452	1541.393414	1488.757386	1386.832211	1436.903217	1352.934775	1446.126565	1414.636678	1431.627278	1322.595288	1324.473512	1317.037715	1314.087715	1223.348640	1042.939059	1120.139928
Tamworth	475.068557	474.697517	448.760723	436.457918	421.964679	440.200738	404.836659	414.618735	406.701043	363.694164	347.631884	326.917746	317.951130	318.231567	290.820254	254.020488	268.838288
Telford and Wrekin	1659.122170	1685.979582	1682.256128	1530.383297	1419.460432	1502.746044	1426.316317	1494.792938	1385.950669	1274.826827	1254.843761	1160.254419	1154.173983	1183.265359	1119.790339	952.853681	1002.876199
Walsall	1841.638129	1829.666727	1772.033335	1673.938533	1488.754727	1624.627863	1525.409270	1549.103445	1531.029950	1380.643572	1300.406164	1250.226818	1242.130066	1201.211275	1119.598232	1009.154672	1088.510816
Warwick	1391.720595	1400.843990	1409.130871	1362.768363	1273.867598	1277.589915	1173.237382	1216.060961	1177.002231	1071.245807	1097.625980	1065.827135	1033.018272	1010.087886	964.526191	797.752307	892.125608
Wolverhampton	1677.179103	1629.700831	1587.437458	1536.582527	1398.213887	1493.486284	1370.306537	1402.082164	1367.714396	1232.190047	1196.524784	1135.863036	1090.199900	1060.553002	989.441812	917.369776	985.990590
Worcester	643.542399	616.473972	657.726991	608.166828	572.476104	601.253296	557.011139	575.389895	551.444982	496.471837	475.843472	447.628667	420.763103	406.786384	381.230693	344.888325	375.993686
Wychavon	1447.518551	1504.532587	1542.140801	1467.778761	1367.405328	1425.701966	1300.782922	1309.649052	1311.106432	1260.514044	1248.715685	1232.972760	1205.932168	1196.208234	1145.581204	1013.927972	1148.046126
Wyre Forest	697.678624	699.747403	750.464334	692.233167	628.671292	664.291809	616.665147	630.668567	612.868789	570.347427	543.650712	522.982812	499.390383	489.710354	458.909804	408.559610	436.541231

Calendar Year	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Local Authority
Birmingham	7059.334372	6952.112034	6800.522322	6744.677640	6099.737055	6412.804284	5805.907414	6094.847957	5928.649539	5283.528982	5187.812112	5018.990855	4952.980766	4694.297182	4500.378294	4177.186378	4480.654084
Bromsgrove	956.642291	966.027098	1014.325992	966.508457	915.239428	943.043254	865.875640	885.439318	875.957613	847.917226	839.358149	828.019311	799.333806	765.570975	756.273104	655.139864	711.760256
Cannock Chase	588.979929	597.874966	584.212829	554.965633	534.431424	565.871617	516.643905	523.651492	521.293200	463.806795	448.224377	432.415392	438.056787	428.182712	400.378294	360.878619	364.042820
Coventry	2285.257172	2244.459643	2109.501041	2028.053404	1835.162506	1962.611578	1766.002096	1881.254900	1815.756821	1664.671871	1647.571233	1574.337656	1548.946359	1496.667926	1428.701726	1280.312165	1344.000349
Dudley	1983.590348	1972.154700	1888.505634	1818.345968	1657.763334	1778.665829	1603.028674	1645.915361	1619.695684	1452.515600	1412.477913	1356.141083	1347.120418	1295.213550	1217.885198	1079.756656	1163.033149
East Staffordshire	1287.270750	1298.775799	1260.546128	1245.328814	1170.588639	1228.732489	1160.773439	1165.877455	1143.246946	1050.973296	1014.291935	988.598271	981.240089	966.664142	903.141998	810.544121	853.521292
Herefordshire, County of	2091.684474	2090.207781	2038.853224	1975.986634	1872.500149	1994.484316	1849.438619	1892.331768	1847.560317	1777.587181	1696.464807	1660.690961	1618.084774	1598.639996	1527.132610	1364.626064	1472.865799
Lichfield	1004.513597	1022.789515	1013.242837	993.374843	955.231477	981.016859	931.692962	938.137617	935.001562	875.810423	863.532062	850.456232	851.408592	837.842706	813.911260	691.404937	754.890754
Malvern Hills	772.773159	799.621230	848.442583	797.397569	757.938462	782.645008	723.285139	726.094378	717.922272	695.654404	688.580938	676.822279	655.588099	642.098370	619.590085	538.434311	601.444378
Newcastle-under-Lyme	1121.822896	1113.654899	1107.267011	1087.895669	1042.573930	1083.119199	1024.749999	1024.635624	1021.924809	939.404697	969.297348	944.169090	929.997916	911.784056	875.110449	784.361909	844.190037
North Warwickshire	1147.478903	1203.041332	1226.193958	1194.492043	1121.940224	1191.290419	1085.180037	1110.955808	1077.930006	1065.117153	1060.260000	1029.729619	1025.527236	1001.285910	961.051901	823.867578	919.630410
Nuneaton and Bedworth	813.097075	815.719471	861.239859	828.813779	772.443801	765.017013	677.858160	704.027486	684.424974	638.996823	634.593829	606.356718	597.691045	593.546030	563.796985	505.938444	540.854821
Redditch	585.906245	591.548822	632.697674	593.920918	533.689582	577.553981	522.724477	528.282173	500.489462	468.059313	444.165840	421.000712	406.630410	394.836203	383.634496	332.680616	357.787785
Rugby	2330.213067	2373.288078	2651.893434	2411.372020	2278.320929	2283.729045	2334.076177	2124.141199	2157.994109	2133.777044	1993.474564	2109.935757	2000.143439	2041.847091	1988.209932	1853.518649	2026.454350
Sandwell	2289.664199	2311.288395	2271.410144	2149.609456	1915.683984	2035.995284	1862.670040	1942.089900	1917.089725	1748.667558	1785.651958	1577.895063	1587.577139	1534.233142	1453.591967	1315.105902	1401.088684
Shropshire	3788.039158	3772.811560	3701.754274	3605.369762	3330.136985	3537.762726	3277.426402	3379.256702	3297.401331	3129.205145	3092.862487	3029.587506	2980.901584	2986.369707	2882.366244	2645.075805	2773.370585
Solihull	1735.803608	1801.630633	1757.701820	1667.849693	1565.457992	1683.199281	1588.399097	1667.768397	1627.491397	1492.473058	1518.294020	1475.326158	1427.841084	1410.483026	1327.805102	1145.680549	1226.181599
South Staffordshire	1142.941882	1174.670664	1167.283617	1104.231784	1101.010671	1130.681369	1083.159234	1088.067517	1082.128759	1028.457756	1000.357801	1002.784932	1034.087269	1000.812994	973.519842	870.868301	954.939263
Stafford	1593.609395	1616.541260	1569.472252	1566.564484	1533.426323	1571.727852	1488.504814	1541.704997	1510.290365	1422.200740	1409.541939	1378.918704	1343.494927	1325.675278	1270.246497	1101.396010	1166.734395
Staffordshire Moorlands	1776.923545	1766.082186	1742.557987	1656.608649	1559.741550	1655.713572	1603.156284	1544.428454	1564.011891	1546.833793	1525.699941	1497.001650	1413.357126	1475.730299	1386.199936	1274.232906	1332.991944
Stoke-on-Trent	2041.728131	2025.362057	1977.856972	1900.940420	1777.333094	1764.352853	1581.980925	1584.479240	1577.177978	1606.524597	1550.265296	1507.331663	1261.650147	1223.080833	1153.929946	1020.164468	1127.356084
Stratford-on-Avon	1501.480094	1513.735452	1541.393414	1488.757386	1386.832211	1436.903217	1352.934775	1446.126565	1414.636678	1431.627278	1322.595288	1324.473512	1317.037715	1314.087715	1223.348640	1042.939059	1120.139928
Tamworth	475.068557	474.697517	448.760723	436.457918	421.964679	440.200738	404.836659	414.618735	406.701043	363.694164	347.631884	326.917746	317.951130	318.231567	290.820254	254.020488	268.838288
Telford and Wrekin	1659.122170	1685.979582	1682.256128	1530.383297	1419.460432	1502.746044	1426.316317	1494.792938	1385.950669	1274.826827	1254.843761	1160.254419	1154.173983	1183.265359	1119.790339	952.853681	1002.876199
Walsall	1841.638129	1829.666727	1772.033335	1673.938533	1488.754727	1624.627863	1525.409270	1549.103445	1531.029950	1380.643572	1300.406164	1250.226818	1242.130066	1201.211275	1119.598232	1009.154672	1088.510816
Warwick	1391.720595	1400.843990	1409.130871	1362.768363	1273.867598	1277.589915	1173.237382	1216.060961	1177.002231	1071.245807	1097.625980	1065.827135	1033.018272	1010.087886	964.526191	797.752307	892.125608
Wolverhampton	1677.179103	1629.700831	1587.437458	1536.582527	1398.213887	1493.486284	1370.306537	1402.082164	1367.714396	1232.190047	1196.524784	1135.863036	1090.199900	1060.553002	989.441812	917.369776	985.990590
Worcester	643.542399	616.473972	657.726991	608.166828	572.476104	601.253296	557.011139	575.389895	551.444982	496.471837	475.843472	447.628667	420.763103	406.786384	381.230693	344.888325	375.993686
Wychavon	1447.518551	1504.532587	1542.140801	1467.778761	1367.405328	1425.701966	1300.782922	1309.649052	1311.106432	1260.514044	1248.715685	1232.972760	1205.932168	1196.208234	1145.581204	1013.927972	1148.046126
Wyre Forest	697.678624	699.747403	750.464334	692.233167	628.671292	664.291809	616.665147	630.668567	612.868789	570.347427	543.650712	522.982812	499.390383	489.710354	458.909804	408.559610	436.541231
Total	49732.222417	49865.040184	49616.825647	47689.374420	44287.997799	46396.818960	43080.033683	44031.880064	43181.893928	40413.744458	39570.616279	38433.656517	37482.255734	36805.003905	35080.103036	31372.640144	33736.855308

Calendar Year	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Local Authority
Birmingham	7059.334372	6952.112034	6800.522322	6744.677640	6099.737055	6412.804284	5805.907414	6094.847957	5928.649539	5283.528982	5187.812112	5018.990855	4952.980766	4694.297182	4500.378294	4177.186378	4480.654084
Bromsgrove	956.642291	966.027098	1014.325992	966.508457	915.239428	943.043254	865.875640	885.439318	875.957613	847.917226	839.358149	828.019311	799.333806	765.570975	756.273104	655.139864	711.760256
Cannock Chase	588.979929	597.874966	584.212829	554.965633	534.431424	565.871617	516.643905	523.651492	521.293200	463.806795	448.224377	432.415392	438.056787	428.182712	400.378294	360.878619	364.042820
Coventry	2285.257172	2244.459643	2109.501041	2028.053404	1835.162506	1962.611578	1766.002096	1881.254900	1815.756821	1664.671871	1647.571233	1574.337656	1548.946359	1496.667926	1428.701726	1280.312165	1344.000349
Dudley	1983.590348	1972.154700	1888.505634	1818.345968	1657.763334	1778.665829	1603.028674	1645.915361	1619.695684	1452.515600	1412.477913	1356.141083	1347.120418	1295.213550	1217.885198	1079.756656	1163.033149
East Staffordshire	1287.270750	1298.775799	1260.546128	1245.328814	1170.588639	1228.732489	1160.773439	1165.877455	1143.246946	1050.973296	1014.291935	988.598271	981.240089	966.664142	903.141998	810.544121	853.521292
Herefordshire, County of	2091.684474	2090.207781	2038.853224	1975.986634	1872.500149	1994.484316	1849.438619	1892.331768	1847.560317	1777.587181	1696.464807	1660.690961	1618.084774	1598.639996	1527.132610	1364.626064	1472.865799
Lichfield	1004.513597	1022.789515	1013.242837	993.374843	955.231477	981.016859	931.692962	938.137617	935.001562	875.810423	863.532062	850.456232	851.408592	837.842706	813.911260	691.404937	754.890754
Malvern Hills	772.773159	799.621230	848.442583	797.397569	757.938462	782.645008	723.285139	726.094378	717.922272	695.654404	688.580938	676.822279	655.588099	642.098370	619.590085	538.434311	601.444378
Newcastle-under-Lyme	1121.822896	1113.654899	1107.267011	1087.895669	1042.573930	1083.119199	1024.749999	1024.635624	1021.924809	939.404697	969.297348	944.169090	929.997916	911.784056	875.110449	784.361909	844.190037
North Warwickshire	1147.478903	1203.041332	1226.193958	1194.492043	1121.940224	1191.290419	1085.180037	1110.955808	1077.930006	1065.117153	1060.260000	1029.729619	1025.527236	1001.285910	961.051901	823.867578	919.630410
Nuneaton and Bedworth	813.097075	815.719471	861.239859	828.813779	772.443801	765.017013	677.858160	704.027486	684.424974	638.996823	634.593829	606.356718	597.691045	593.546030	563.796985	505.938444	540.854821
Redditch	585.906245	591.548822	632.697674	593.920918	533.689582	577.553981	522.724477	528.282173	500.489462	468.059313	444.165840	421.000712	406.630410	394.836203	383.634496	332.680616	357.787785
Rugby	2330.213067	2373.288078	2651.893434	2411.372020	2278.320929	2283.729045	2334.076177	2124.141199	2157.994109	2133.777044	1993.474564	2109.935757	2000.143439	2041.847091	1988.209932	1853.518649	2026.454350
Sandwell	2289.664199	2311.288395	2271.410144	2149.609456	1915.683984	2035.995284	1862.670040	1942.089900	1917.089725	1748.667558	1785.651958	1577.895063	1587.577139	1534.233142	1453.591967	1315.105902	1401.088684
Shropshire	3788.039158	3772.811560	3701.754274	3605.369762	3330.136985	3537.762726	3277.426402	3379.256702	3297.401331	3129.205145	3092.862487	3029.587506	2980.901584	2986.369707	2882.366244	2645.075805	2773.370585
Solihull	1735.803608	1801.630633	1757.701820	1667.849693	1565.457992	1683.199281	1588.399097	1667.768397	1627.491397	1492.473058	1518.294020	1475.326158	1427.841084	1410.483026	1327.805102	1145.680549	1226.181599
South Staffordshire	1142.941882	1174.670664	1167.283617	1104.231784	1101.010671	1130.681369	1083.159234	1088.067517	1082.128759	1028.457756	1000.357801	1002.784932	1034.087269	1000.812994	973.519842	870.868301	954.939263
Stafford	1593.609395	1616.541260	1569.472252	1566.564484	1533.426323	1571.727852	1488.504814	1541.704997	1510.290365	1422.200740	1409.541939	1378.918704	1343.494927	1325.675278	1270.246497	1101.396010	1166.734395
Staffordshire Moorlands	1776.923545	1766.082186	1742.557987	1656.608649	1559.741550	1655.713572	1603.156284	1544.428454	1564.011891	1546.833793	1525.699941	1497.001650	1413.357126	1475.730299	1386.199936	1274.232906	1332.991944
Stoke-on-Trent	2041.728131	2025.362057	1977.856972	1900.940420	1777.333094	1764.352853	1581.980925	1584.479240	1577.177978	1606.524597	1550.265296	1507.331663	1261.650147	1223.080833	1153.929946	1020.164468	1127.356084
Stratford-on-Avon	1501.480094	1513.735452	1541.393414	1488.757386	1386.832211	1436.903217	1352.934775	1446.126565	1414.636678	1431.627278	1322.595288	1324.473512	1317.037715	1314.087715	1223.348640	1042.939059	1120.139928
Tamworth	475.068557	474.697517	448.760723	436.457918	421.964679	440.200738	404.836659	414.618735	406.701043	363.694164	347.631884	326.917746	317.951130	318.231567	290.820254	254.020488	268.838288
Telford and Wrekin	1659.122170	1685.979582	1682.256128	1530.383297	1419.460432	1502.746044	1426.316317	1494.792938	1385.950669	1274.826827	1254.843761	1160.254419	1154.173983	1183.265359	1119.790339	952.853681	1002.876199
Walsall	1841.638129	1829.666727	1772.033335	1673.938533	1488.754727	1624.627863	1525.409270	1549.103445	1531.029950	1380.643572	1300.406164	1250.226818	1242.130066	1201.211275	1119.598232	1009.154672	1088.510816
Warwick	1391.720595	1400.843990	1409.130871	1362.768363	1273.867598	1277.589915	1173.237382	1216.060961	1177.002231	1071.245807	1097.625980	1065.827135	1033.018272	1010.087886	964.526191	797.752307	892.125608
Wolverhampton	1677.179103	1629.700831	1587.437458	1536.582527	1398.213887	1493.486284	1370.306537	1402.082164	1367.714396	1232.190047	1196.524784	1135.863036	1090.199900	1060.553002	989.441812	917.369776	985.990590
Worcester	643.542399	616.473972	657.726991	608.166828	572.476104	601.253296	557.011139	575.389895	551.444982	496.471837	475.843472	447.628667	420.763103	406.786384	381.230693	344.888325	375.993686
Wychavon	1447.518551	1504.532587	1542.140801	1467.778761	1367.405328	1425.701966	1300.782922	1309.649052	1311.106432	1260.514044	1248.715685	1232.972760	1205.932168	1196.208234	1145.581204	1013.927972	1148.046126
Wyre Forest	697.678624	699.747403	750.464334	692.233167	628.671292	664.291809	616.665147	630.668567	612.868789	570.347427	543.650712	522.982812	499.390383	489.710354	458.909804	408.559610	436.541231

	Calendar Year	Grand Total
0	2005	656867.315366
1	2006	649603.223057
2	2007	634904.263654
3	2008	614046.665833
4	2009	558455.613837
5	2010	571129.481835
6	2011	525842.800141
7	2012	544172.639988
8	2013	531229.165974
9	2014	491490.767041
10	2015	475077.919255
11	2016	449383.499987
12	2017	437824.383087
13	2018	430745.509283
14	2019	416856.663324
15	2020	376807.810496
16	2021	399046.140782

	NC Sector	Q1 2008	Q2 2008	Q3 2008	Q4 2008	Q1 2009	Q2 2009	Q3 2009	Q4 2009	Q1 2010	...	Q3 2020	Q4 2020	Q1 2021	Q2 2021	Q3 2021	Q4 2021	Q1 2022	Q2 2022	Q3 2022	Q4 2022
0	Energy supply	58.3	49.7	46.5	58.4	58.3	41.7	39.4	50.5	55.3	...	18.9	20.8	21.6	19.1	19.6	20.5	20.6	19.9	21.3	20.3
1	Business	26.8	21.1	17.5	23.2	23.2	16.5	15.7	20.4	23.5	...	13.3	16.8	19.3	14.9	13.4	16.9	18.9	13.8	12.9	16.4
2	Transport	33.4	32.9	32.8	31.5	30.9	31.9	32.2	30.6	29.6	...	25.6	26.1	21.9	27.5	29.5	29.5	27.0	29.5	28.2	27.8
3	Public	3.4	2.3	1.2	2.8	2.8	1.8	1.4	2.9	3.0	...	1.0	2.1	2.8	1.3	1.1	2.3	2.8	1.3	1.2	2.3
4	Residential	31.0	13.3	7.3	26.7	31.0	11.3	7.2	25.6	34.8	...	5.2	22.5	28.3	13.8	4.9	20.5	24.0	9.8	4.8	17.9
5	Other sectors [note 8]	4.6	4.4	4.3	4.3	3.0	2.9	2.9	3.0	3.1	...	2.2	2.3	2.7	2.6	2.6	2.8	2.8	2.6	2.6	2.9
6	Total CO2	157.5	123.9	109.6	146.9	149.3	106.1	98.7	133.0	149.2	...	66.2	90.6	96.6	79.3	71.1	92.6	96.0	77.0	70.9	87.6
7	Other greenhouse gases [note 6]	29.5	29.5	29.5	29.5	28.0	28.0	28.0	28.0	27.0	...	21.9	21.9	21.7	21.7	21.7	21.7	21.4	21.4	21.4	21.4
8	Total greenhouse gas emissions	187.0	153.4	139.1	176.4	177.3	134.1	126.7	161.0	176.2	...	88.2	112.5	118.4	101.0	92.8	114.3	117.4	98.4	92.3	109.0

Dep. Variable:	Emissions	No. Observations:	60
Model:	ARIMA(1, 1, 1)	Log Likelihood	-321.549
Date:	Thu, 11 Jan 2024	AIC	649.099
Time:	08:42:31	BIC	655.331
Sample:	01-01-2008	HQIC	651.532
	- 10-01-2022
Covariance Type:	opg

	coef	std err	z	P>\|z\|	[0.025	0.975]
ar.L1	0.1138	0.170	0.669	0.503	-0.220	0.447
ma.L1	-0.8330	0.132	-6.296	0.000	-1.092	-0.574
sigma2	3122.7480	1132.184	2.758	0.006	903.707	5341.789

Ljung-Box (L1) (Q):	0.04	Jarque-Bera (JB):	6.42
Prob(Q):	0.83	Prob(JB):	0.04
Heteroskedasticity (H):	0.47	Skew:	-0.00
Prob(H) (two-sided):	0.10	Kurtosis:	1.38